Abstract It is an immensely challenging task to segment the tissue regions and cells in histological images of breast cancer with precision, but the results of this task are extremely significant for the field of computational pathology as a whole. To address this challenge, the integration of the Denoising Diffusion Probabilistic Model (DDPM) and the Generative Adversarial Network (GAN) has been explored. Specifically, we employ a conditional DDPM as the generator within the GAN framework, alongside a conditional adversarial network serving as the discriminator, to achieve segmentation of breast cancer histology images both at the regional and cellular levels. The forward process of the DDPM is first applied to the image mask. As the noise is added step by step, it is conditioned with the pathological image and estimated by a denoising network. To improve the estimated noise, the estimated noise is again conditioned with the pathological image and fed into the discriminator as part of the training process. As part of the test phase, a noise image conditioned with a pathological image is fed into a denoising model trained taking into consideration each time step, which segments the images into regions and cells in the reverse process. Three datasets were used for the experiments, one at a regional level and two at a cellular level. This method outperforms both GAN and diffusion models, as well as current state-of-the-art methods. Specifically, our method shows notable improvements in terms of Dice and IoU metrics over existing state-of-the-art methods.

Over the last decade, biometrics has witnessed significant advancements in various forensic and security applications for human identification and authentication, with growing interest in effective and discriminative traits such as palmprints. However, practical applications still face challenges, especially when palmprints are collected in portions, such as at crime scenes, or partially acquired for authentication in uncontrolled environments. This paper presents a novel method that incorporates the local binary patterns (LBP) operator into the conventional scale-invariant feature transform (SIFT) algorithm to detect and extract robust keypoint features. While SIFT employs a Gaussian filter to detect keypoints on the palmprint, the proposed method leverages the multi-scale LBP operator to detect stable points prior to computing the corresponding descriptors. Furthermore, an efficient method for filtering keypoints, namely the Self-Geometric Relationship (SGR) filter, is introduced to eliminate potential false matches. The proposed palmprint recognition system, LBPSIFT-SGR, demonstrates competitive performance on full palmprints compared to state-of-the-art techniques and exhibits clear superiority on partial palmprint images, where competing systems fail, across different datasets.

Malicious Software (Malware) is a primary element of many cyber crimes and attacks, causing massive damage and financial losses to organizations. Accordingly, malware detection and classification has become a crucial security field resulting in various attempts from researchers to develop solutions including signature-based approaches to Artificial Intelligence (AI) models showing their efficacy in detecting malware. Yet, users still have reservations about AI models due to the ambiguity and mysteriousness of their decisions resulting from their black-box nature. To address this problem, this paper proposes to develop explainable AI models to classify the malware families robustly. The proposed method uses text classification of API call sequences generated by these families by considering two datasets. A weighted training methodology is used to solve the dataset imbalance problem. Subsequently, the method presents an eXplainable AI (XAI) approach to establish an understandable and interpretable relationship between the API call sequences and the Bidirectional Encoder Representations from Transformers (BERT) model decisions, which enhance the model accountability and usability by employing the Local Interpretable Model-Agnostic Explanation (LIME) and the Shapley Additive Explanations (SHAP) platforms. The results reveal that the BERT model outperforms its counterparts considering F1 score, Balanced Accuracy (BA), and Matthews correlation coefficient (MCC).

Abstract In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.

The domain of image classification has been seen to be dominated by high-performing deep-learning (DL) architectures. However, the success of this field, as seen over the past decade, has resulted in the complexity of modern methodologies scaling exponentially, commonly requiring millions of parameters. Quantum computing (QC) is an active area of research aimed toward greatly reducing problems of complexity faced in classical computing. With growing interest toward quantum machine learning (QML) for applications of image classification, many proposed algorithms require usage of numerous qubits. In the noisy intermediate-scale quantum (NISQ) era, these circuits may not always be feasible to execute effectively; therefore, we should aim to use each qubit as effectively and efficiently as possible, before adding additional qubits. This article proposes a new single-qubit-based deep quantum neural network for image classification that mimics traditional convolutional neural network (CNN) techniques, resulting in a reduced number of parameters compared with previous works. Our aim is to prove the concept of the initial proposal by demonstrating classification performance of the single-qubit-based architecture, as well as to provide a tested foundation for further development. To demonstrate this, our experiments were conducted using various datasets including MNIST, Fashion-MNIST, and ORL face datasets. To further our proposal in the context of the NISQ era, our experiments were intentionally conducted in noisy simulation environments. Initial test results appear promising, with classification accuracies of 94.6%, 89.5%, and 82.5% achieved on the subsets of MNIST, FMNIST, and ORL face datasets, respectively. In addition, proposals for further investigation and development were considered, where it is hoped that these initial results can be improved.

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets.

Currently, most potential field models for decentralised control of multiagent swarms use only single-valued parameters for the computation of control vectors . This restriction often limits the structures that can evolve, since agents are unable to modify their behaviour based on their structural role. This paper proposes an enhanced model that uses the perimeter status of agents in selecting control parameters. This allows a wider variety of emergent behaviours, many of which result in much improved swarm structures. The model is based upon equivalence classes of agent pairs, defined by their perimeter status. Array-valued parameters are introduced to allow each equivalence class to be given its own parameter values. The model also introduces a new control vector to ‘flatten’ reflex angles between neighbouring agents on the swarm perimeter, often leading to significantly improved swarm structure. Extensive experiments have been conducted that demonstrate how the new model causes a variety of useful behaviours to emerge from random swarm deployments. The results show that several important behaviours, such as shape control, void removal, perimeter packing and expansion, and perimeter rotation, can be produced without the need for explicit inter-agent communication. The approach is applicable to a variety of applications, including reconnaissance , area-coverage, and containment.

Abstract Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one.

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and Machine Learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. The experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

Abstract Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media covers. One of the covers that is gaining interest in the field is video. Presently, the global IP video traffic forms the major part of all consumer Internet traffic. It is also gaining attention in the field of digital forensics and homeland security in which threats of covert communications hold serious consequences. Thus, steganography technicians will prefer video to other types of covers like audio files, still images, or texts. Moreover, video steganography will be of more interest because it provides more concealing capacity. Contrariwise, investigation in video steganalysis methods does not seem to follow the momentum even if law enforcement agencies and governments around the world support and encourage investigation in this field. In this paper, the authors review the most important methods used so far in video steganalysis and sketch the future trends. To the best of the authors’ knowledge this is the most comprehensive review of video steganalysis produced so far.

Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general disease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme, while data privacy has been a primary concern toward a wider exploitation of Electronic Health and Medical Records (EHR/EMR) over cloud-based medical services. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trustworthy edge service for grading the severity of PD in patients.

Abstract Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

Abstract Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.

Stock price manipulation, a major problem in capital markets surveillance, uses illegitimate means to influence the price of traded stocks in order to reap illicit profit. Most of the existing attempts to detect such manipulations have either relied upon annotated trading data, using supervised methods, or have been restricted to detecting a specific manipulation scheme. There have been a few unsupervised algorithms focusing on general detection yet none of them explored the innate affinity among the stock trades, be it normal or manipulative. This paper proposes a fully unsupervised model based on the idea of learning the relationship among stock prices in the form of an affinity matrix. The proposed affinity matrix based features are used to train an under-fitting autoencoder in order to learn an efficient representation of the normal stock prices. A kernel density estimate of the normal trading data is used as the reconstruction error of the autoencoder. During the detection phase, the normal dataset has been injected with synthetic manipulative trades. A kernel density estimation based clustering technique is then used to detect manipulative trades based on their autoencoder representation. The proposed approach is validated on benchmark stock price data from the LOBSTER project and the obtained results show dramatic improvements in the detection performance over existing price manipulation detection techniques.

Abstract Over the past two decades, researchers in the field of biometrics have presented a wide variety of coding-based palmprint recognition methods. These approaches mainly rely on extracting the texture features, e.g. line orientations, and phase information, using different filters. In this paper, we propose a new efficient palmprint recognition method based on the Different of Block Means. In the proposed scheme, only basic operations (i.e. mainly additions and subtractions) are used, thus involving a much lower computational cost when compared with existing systems. This makes the system suitable for online palmprint identification and verification. Furthermore, the technique has been shown to deliver superior performance over related systems.

Person re-identification is a challenging retrieval task that aims to match pedestrians from multiple non-overlapping cameras. In this paper, we introduce a deep multi-instance learning framework to aggregate instance-level images to boost retrieval performance. Considerable annotation inconsistency inevitably happens in many current person re-identification datasets due to unconcerned of annotations or dramatic varieties in surveillance scenarios, thereby leading to model drifting. To alleviate this issue, we formulate the person re-identification problem in a weakly supervised setting, and propose a self-inspired attention model based on Bayesian inference, to adaptively evaluate regional features with their global dependencies across instances, which we refer to as Bayesian Inferred Self-Attentive Aggregation (BISAA). The evaluation mechanism is parameterized by neural networks to provide an insight into the contribution of each instance and semantic human part to set-level labels. Furthermore, to facilitate aggregation across a set of instances, we propose a new collective aggregation function to make the model more robust to outliers, by adjusting the activation threshold, to allow some non-informative instances to be ignored while paying more attention to the discriminative ones. Extensive experiments with ablation analysis show the effectiveness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: PRID2011, iLIDS-VID, Market-1501 and MSMT17.

Market manipulation is the act of artificially influencing the price of a security to make profit through illegitimate schemes. It is evident from the literature that only a handful of methods had been proposed for stock market manipulation detection. Most of those methods either used supervised training or focused only on specific manipulation schemes. This paper introduces a semi-supervised learning method based on a hybridization of an altered dendritic cell immune system inspired approach and Kernel Density Estimation based clustering technique. Dendritic Cell Algorithm (DCA) mimics the human immune system in data processing using the danger theory model for anomaly detection. An important advantage of the proposed approach is that the DCA is adapted for scaling down the dimension of the input data set to a set of only three outputs that are then clustered using KDE clustering. This avoids the need for assigning different threshold parameters as in a conventional DCA, hence automating the detection process. Another important advantage is that supervised training is not required for signal categorization during the preprocessing phase of DCA. The proposed approach is validated on Level 1 stock price tick data obtained from the LOBSTER project which contains highly volatile and high frequency trading (HFT) time series. The considered manipulation schemes are Pump and Dump and Gouging or Spoof trading. The proposed approach is benchmarked against existing stock market manipulation detection approaches as well as existing anomaly detection techniques based on KNN, OCSVM, PCA and k-means. The obtained results show substantial improvements in terms of the area under the ROC curve (AUC) and the false alarm rate.

In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion to generate random transformations of images with missing pixels to expand existing labelled datasets. In our work, GAN’s been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on GAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showed between DCGAN cascaded with SSD and SSD itself.

Local binary pattern (LBP) algorithm and its variants have been used extensively to analyse the local textural features of digital images with great success. Numerous extensions of LBP descriptors have been suggested, focusing on improving their robustness to noise and changes in image conditions. In our research, inspired by the concepts of LBP feature descriptors and a random sampling subspace, we propose an ensemble learning framework, using a variant of LBP constructed from Pascal’s coefficients of n-order and referred to as a multiscale local binary pattern. To address the inherent overfitting problem of linear discriminant analysis, PCA was applied to the training samples. Random sampling was used to generate multiple feature subsets. In addition, in this work, we propose a new feature extraction technique that combines the pyramid histogram of oriented gradients and LBP, where the features are concatenated for use in the classification. Its performance in recognition was evaluated using the Hong Kong Polytechnic University database. Extensive experiments unmistakably show the superiority of the proposed approach compared to state-of-the-art techniques.

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions.

Handwriting biometrics is the science of identifying the behavioral aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines scale-invariant feature transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMMs). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While an SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates an SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic, and one hybrid language), and the results have shown the superiority of the proposed system over the state-of-the-art techniques.

Frontal view gait recognition for people identification has been carried out using single RGB, stereo RGB, Kinect 1.0, and Doppler radar. However, existing methods based on these camera technologies suffer from several problems. Therefore, we propose a four-part method for frontal view gait recognition based on the fusion of multiple features acquired from a Time-of-Flight (ToF) camera. We have developed a gait data set captured by a ToF camera. The data set includes two sessions recorded seven months apart, with 46 and 33 subjects, respectively, each with six walks with five covariates. The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. Rigorous experiments are carried out to compare the proposed method with state-of-the-art methods. The results show distinct improvements over recognition rates for all covariates. The proposed method outperforms all major existing approaches for all covariates and results in 66.1% and 81.0% Rank 1 and Rank 5 recognition rates, respectively, in overall covariates, compared with a best state-of-the-art method performance of 35.7% and 57.7%.

Separating content from noise in historical manuscripts is a fundamental task in digital palaeography. This study presents a fully automated segmentation approach based on the response of Harris corner detectors. The strength and clustering efficiency of the detected corners in the manuscripts are evaluated and used to segment the content from the background and noise. In addition, a manuscript reconstruction technique is proposed from the gradient field using the Poisson method to guide the interpolation. This reconstruction is able to remove noise significantly and hence enhances the contrast of the content thus making it easier for users to read and process these documents. The proposed approaches are evaluated using various standard databases to highlight their effectiveness and robustness to a multitude of noise and writing styles. Subjective and objective evaluations of the experimental results show that these techniques are able to successfully segment and reconstruct a very diverse set of scanned documents. An analysis of the results has also shown that the proposed technique compares favourably against similar counterparts.

The age of a historical manuscript can be an invaluable source of information for paleographers and historians. The process of automatic manuscript age detection has inherent complexities, which are compounded by the lack of suitable datasets for algorithm testing. This paper presents a dataset of historical handwritten Arabic manuscripts designed specifically to test state-of-the-art authorship and age detection algorithms. Qatar National Library has been the main source of manuscripts for this dataset while the remaining manuscripts are open source. The dataset consists of over 2000 images taken from various handwritten Arabic manuscripts spanning fourteen centuries. In addition, a sparse representation-based approach for dating historical Arabic manuscript is also proposed. There is lack of existing datasets that provide reliable writing date and author identity as metadata. KERTAS is a new dataset of historical documents that can help researchers, historians and paleographers to automatically date Arabic manuscripts more accurately and efficiently.

Advancing technologies in the current era paved a lot to break the hurdles in medical diagnostic field. When cancer turned out to be the most common and dangerous disease of the age, novel diagnostic methodologies were introduced to enable early detection and hence save numerous lives. Accomplishment of various automatic and semi-automatic approaches in the diagnosis has proved its sufficient impetus to improve diagnostic speed and accuracy. A wide range of image processing based tools are currently available as a part of automatic cancer detection systems. Different imaging modalities have been utilized for extracting the suspected patient information, where the multispectral imaging has emerged as an efficient means for capturing the entire range of spectral and spatial data. In this paper, we review the current multispectral imaging based methods for automatic diagnosis of major types of cancer and discuss the limitations which are yet to be overcome, so as to improve the existing systems.

With a rapidly increasing incidence of melanoma skin cancer, there is a need for decision support systems to detect it in its early stages, which would lead to better decisions in treating it successfully. However, developing such systems is still a challenging task for researchers. Several Computer Aided-Diagnosis (CAD) systems have been proposed in the last two decades to increase the accuracy of melanoma detection. Image feature extraction is a critical step in differentiating between melanoma and normal skin lesions. In this paper, we propose to improve a bag-of-words approach by combining features consisting of the color histogram and first order moments with the Histogram of Oriented Gradients (HOG). Experimental results show that the proposed technique significantly improves the detection accuracy, with an average sensitivity of 91% and specificity of 85%. The proposed system was validated on a dataset of 200 medically annotated images (40 melanomas and 160 non-melanomas) obtained from the database of the Hospital Pedro Hispano. [1].

Identification of functional characteristics of the virus-antibody interplay in individuals can provide insight to the development of effective vaccines against HIV virus. In order to reveal the functional interactions between human immune system and HIV virus, computational methods such as clustering, classification, feature selection and regression methods can be utilised to construct predictive models. The purpose of this study is to predict the associations between antibody features and effector function activities on RV144 vaccine recipients. The RV144 vaccine dataset contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. In this study, we proposed a novel supervised feature selection framework to identify the discriminating antibody features from RV144 vaccine dataset. Then, the Support Vector Regression is utilised to quantitatively predict the association between antibody features (IgGs) and effector function activities. Three different cell-mediated assays are utilised in this study to characterise effector function activities: antibody dependent cellular phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC), and natural killer cell cytokine release. Promising experimental results on these three cell-based assays have validated the effectiveness of our proposed framework. The prediction performance of proposed feature selection framework is compared to the previous studies which utilised the RV144 dataset for the same purpose.

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.

Although visual surveillance has emerged as an effective technology for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacy-related applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain, thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest and, then, applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

With the rapid development of Internet-of-Things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently, in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize the traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper, we propose a new ensemble approach-many-kernel random discriminant analysis (MK-RDA)-to discover discriminative patterns from the chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle the chaotic facial patterns in the scrambled domain, where the random selections of features are made on semantic components via salience modeling. In our experiments, the proposed MK-RDA was tested rigorously on three human face data sets: the ORL face data set, the PIE face data set, and the PUBFIG wild face data set. The experimental results successfully demonstrate that the proposed scheme can effectively handle the chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in the emerging IoT applications.

Photo-response non-uniformity (PRNU) noise has been well established as a reliable fingerprint of imaging sensors for source camera identification and other forensic applications. In this paper, we introduce a novel denoising method for PRNU noise extraction that considerably outperforms the existing techniques. The rationale is to use as little as one adjacent pixel in spatial domain filtering to suppress the pixel-to-pixel correlation in the estimation of the (supposedly white) PRNU noise. Experimental results are presented based on an image database of ten cameras of different models and makes. These results are embodied in the receiver operating characteristic (ROC) of source camera identification.

Computational methods such as clustering, classification and regression methods can be applied in immunoin-formatics to construct predictive models to reveal relationships between antibody features and their functional outcomes. This paper studies the effect of antibody features and the functional outcome obtained on RV144 vaccine recipients. The RV144 vaccine data set contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. Unlike semi-supervised and supervised feature selection methods, unsupervised feature selection methods provide unbiased approach as they are not dependent to response variable. In this paper, four different unsupervised feature selection methods are used in order to reveal the discriminating antibody features. Then, the support vector based methods are used in order to predict natural killer (NK) cell cytokine release effect. The results yield a high correlation coefficient as much as 0.59 and 0.72 for the support vector based regression (SVR) and classification (SVM) predictive models, respectively.

This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation of EEG signals and are considered and processed as an image using T-F image processing techniques. The proposed features include shape and texture-based descriptors and are able to describe visually the normal and seizure activity patterns observed in T-F images. The results obtained on real EEG data show that T-F image descriptor-based features achieve an overall classification accuracy of up to 98% for 100 EEG segments using one-against-one SVM classifier. The results suggest that the proposed method outperforms those methods, which employ signal features only or combined signal-image features by about 3% for 100 EEG signals.

This paper presents novel time-frequency (t-f) feature extraction approach for the classification of EEG signals for Epileptic seizure activities detection. The proposed features are based on Local Binary Patterns (LBP) descriptor extracted from t-f representation of EEG signals processed as a textured image. Compared to most previous t-f approaches were based only on features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands, the proposed t-f features are capable to describe visually the epileptic seizure activity patterns observed in t-f image of EEG signals. The results obtained on real EEG data show that the use of t-f LBP descriptor-based features achieve an overall classification accuracy up to 99% for 150 EEG signals using 2-class SVM classifier. This is confirmed by ROC curve analysis.

Real-time traffic analysis using the road mounted surveillance cameras present multitude of benefits. This kind of traffic video processing has become an important means for intelligent traffic management and control. The estimation and analysis of road traffic motion is an involved task in computer vision and video processing. In our work, morphological operations and region growing method are used to perform salient motion detection of objects. In classical background extraction method, the background has to be learnt from large numbers of frames. In our method, no a prior knowledge about shape and size of object is acquired. Instead, sum of square difference is estimated via online learning for the calculation of the centroid distance. The test results indicate that the road vehicles and their statistics are determined through our algorithm with complete fidelity.

Palmprint is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition mode can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactless devices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose a ROI extraction method that addresses this issue. We test our method on two databases PolyU and CASIA which illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases. Performances obtained with the proposed palmprint system are promising.

In this paper, we propose a novel approach for writer identification using codebook generation based on text skeletonization.Unlike other schemes, the skeleton in this approach is segmented at its junction pixels into elementary graphic units called graphemes. The codebook is generated by clustering the graphemes according to their distributions into a predefined grid. This method has been evaluated using the benchmarking dataset of the International Conference on Document Analysis and Recognition (ICDAR 2011) writer identification contest and has shown promising results. We also studied the effect of the amount of handwriting on the identification accuracy of the method and demonstrated that the proposed method is valid for Latin and Greek languages.

This paper presents new time-frequency (T-F) features to improve the detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from the T-F representation of EEG signals, are considered and processed as an image using image processing techniques. The idea of the proposed feature extraction method is based on the application of Otsu's thresholding algorithm on the T-F image in order to detect the regions of interest where the epileptic seizure activity appears. The proposed T-F image related-features are then defined to describe the statistical and geometrical characteristics of the detected regions. The results obtained on real EEG data suggest that the use of T-F image based-features with signal related-features improve significantly the performance of the EEG seizure detection and classification by up to 5% for 120 EEG signals, using a multi-class SVM classifier.

Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels. When each sub-word or Part of Arabic Word (PAW) within the image being investigated is processed at multiple threshold levels a cluster graph is obtained where each cluster represents the individual sub-words of that word. Once the clusters are obtained the task of segmentation is managed by simply selecting the respective cluster automatically which is achieved using the 95% confidence interval on the processed data generated by the accumulated graph. The presented algorithm was tested on 537 randomly selected words from the AHTID/MW database and the results showed that 95.3% of the sub-words or PAW were correctly segmented and extracted. The proposed method has shown considerable improvement over the projection profile method which is commonly used to segment sub-words or PAW.

Face hallucination can be a useful tool for visualizing a low quality face into a visually better quality, making it an attractive technology for many applications. While faces in surveillance videos are usually at very low resolution, in this paper, we propose to use face hallucination technology to visualize faces from visual surveillance systems, and develop a weighted scheme to enhance the quality of face visualization from surveillance videos. Our experiment validated that in comparison with the classic eigenspace based face hallucination, our proposed weighted face hallucination strategy can help improve the overall quality of a facial image extracted from surveillance footage.

This paper proposes a robust minutiae based fingerprint image hashing technique. The idea is to incorporate the orientation and descriptor in the minutiae of fingerprint images using SIFT-Harris feature points. A recent shape context based perceptual hashing method has been compared against the proposed technique. Experimentally, the proposed technique has been shown to deliver better robustness against image processing operations including JPEG lossy compression and geometric attacks such as rotation and translation.

This paper proposes new time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. These features are obtained by translating and combining the most relevant time-domain and frequency-domain features into a joint time-frequency domain in order to improve the performance of EEG seizure detection and classification of non-stationary EEG signals. The optimal relevant translated features are selected according maximum relevance and minimum redundancy criteria. The experiment results obtained on real EEG data, show that the use of the translated and the selected relevant time-frequency features improves significantly the EEG classification results compared against the use of both original time-domain and frequency-domain features.

Intermodality face matching or Heterogeneous face recognition involves matching faces from different modalities such as infrared images, sketch images and low/high resolution visual images. This problem is further alleviated due to inherit problems in face recognition such as pose, expression, illumination, occlusion etc. Existing face recognition algorithms fail to address the existing feature gap exist between images of different modalities. To solve this problem, we propose a new method inspired from Probabilistic Linear Discriminant Analysis (PLDA). PLDA is a generative probabilistic method which models the face into signal and noise components. This method reports outstanding results when compared to other contemporary approaches. But PLDA is designed to apply the image data in only one modality. In this paper, its efficacy has been extended to more generic problem of handling faces captured in different modalities. Experiments conducted on HFB (VIS-NIR), Biosecure (Low-High or Webcam-Digitalcam) face databases validate its robustness and superiority over other methods.

Sensor pattern noise has been broadly used in the literature for image authentication and source camera identification. The abundant information that a sensor pattern noise carries in terms of the frequency content makes it unique and hence suitable for source camera identification. The traditional approach for estimating the sensor pattern noise uses a set of images to estimate a pattern residual signal from each image. The estimated residual signals are then averaged to obtain the sensor pattern noise. This is based on the assumption that each residual signal is a noisy observation of the sensor pattern noise. Such an assumption is well justified in practice because the images are acquired under different conditions making the corresponding residual signals distinct from each other. For instance bright images provide better sensor pattern noise estimation than dark images. Also, saturated pixels cause undesirable noise in residual signals. Inspired by this observation, a weighted averaging approach is proposed for efficient sensor pattern noise estimation. The proposed approach has been validated with two sensor pattern noise estimation techniques from the literature and significant improvements have been shown through experimental results.

In current society, reliable identification and verification of individuals are becoming more and more necessary tasks for many fields, not only in police environment, but also in civilian applications, such as access control or financial transactions. Biometric systems are used nowadays in these fields, offering greater convenience and several advantages over traditional security methods based on something that you know (password) or something that you have (keys). In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint (MSP) images using Contourlet Transform (CT) and Gabor Filter (GF) response. In this study, the spectrum image is characterized by the contourlet coefficients sub-bands. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same spectrum is filtered by the Gabor filter. The real and imaginary responses of the filtering image are used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. Our experimental results show the effectiveness and reliability of the proposed method, which brings both high identification and accuracy rate.

This paper proposes a semi-non parametric density estimation framework for high-dimensional data. Dimensionality reduction is achieved by reorganizing the domain variables set into a junction tree of cliques each containing a small number of variable

Stroke recovery from offline handwriting is a very interesting research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in stroke recovery from offline handwriting. This competition has been hosted on Kaggle, it has attracted 45 teams from both academia and industry. This paper gives details on this competition, including the dataset used, the evaluation procedure and description of participating methods and their performances.

This study proposes an efficient algorithm for colour image compression with listless implementation based on set partition block embedded coding (SPECK). The objective of this work is to develop an algorithm that exploits the redundancy in colour spaces, low complexity quadtree partitioning and reduced memory requirements. Colour images are first transformed into luminance chrominance (YCbCr) planes and a wavelet transform is applied. A reduction of the memory requirement is achieved with the introduction of a state marker that matches each colour plane to eliminate the list with dynamic memory in the original colour SPECK coder (CSPECK). The wavelet coefficients are scanned using Z ‐order that matches the subband decompositions. The proposed algorithm then encodes the de‐correlated colour plane as one unit and generates a mixed bit stream. The linear indexing and initial state marker are modified to jointly test the chrominance plane together. Composite colour coding enables precise control of the bit rate. The performance of the proposed algorithm is comparable with CSPECK, set partitioning in hierarchical trees (SPIHT) and JPEG2000 but with less memory requirements. For progressive lossless, a saving of more than 70% than final working memory against CSPECK and SPIHT highlights the benefit of the proposed algorithm.

Visual concept detection and action recognition are one of the most important tasks in content-based multimedia information retrieval (CBMIR) technology. It aims at annotating images using a vocabulary defined by a set of concepts of interest including scenes types (mountains, snow, etc.) or human actions (phoning, playing instrument). This paper describes our system in the ImageCLEF@ICPR10, Pascal VOC 08 Visual Concept Detection and Pascal VOC 10 Action Recognition Challenges. The proposed system ranked first in these large-scale tasks when evaluated independently by the organizers. The proposed system involves state-of-the-art local descriptor computation, vector quantization via clustering, structured scene or object representation via localized histograms of vector codes, similarity measure for kernel construction and classifier learning. The main novelty is the classifier-level and kernel-level fusion using Kernel Discriminant Analysis and Spectral Regression (SR-KDA) with RBF Chi-Squared kernels obtained from various image descriptors. The distinctiveness of the proposed method is also assessed experimentally using a video benchmark: the Mediamill Challenge along with benchmarks from ImageCLEF@ICPR10, Pascal VOC 10 and Pascal VOC 08. From the experimental results, it can be derived that the presented system consistently yields significant performance gains when compared with the state-of-the art methods. The other strong point is the introduction of SR-KDA in the classification stage where the time complexity scales linearly with respect to the number of concepts and the main computational complexity is independent of the number of categories.

Biometrics is an effective technology for personnel identity recognition, but uni-modal biometric systems which use a single trait for recognition will suffer from problems like noisy sensor data, non-universality, lack of distinctiveness of the biometric trait, and spoof attacks. These problems can be tackled by using multi-biometrics in the system. Hand-based person recognition provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. As one of the most popular biometric traits, fingerprints (FP) are widely used in personal recognition. However, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. In this paper, FP and FKP are integrated in order to construct an efficient multi-biometric recognition system based on matching score level and image level fusion. In this study we use the minimum average correlation energy (MACE) and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane performance measures, max peak value and peak-to-sidelobe ratio, to determine the effectiveness of this method. The experimental results showed that the designed system achieves an excellent recognition rate on the Hong Kong polytechnic university (PolyU) FKP and high resolution fingerprint database.

This paper is concerned with an application of ICA for a possible improvement of iris recognition by replying to the question: does ICA perform well for such purpose? To achieve this, the hypotheses and the theoretical concepts of ICA methods used ar

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. This paper presents active fingerprinting which combines robust watermarking and independent Gaussian fingerprints generated by Tardos algorithm to secure the distributed copy of the H.264 /AVC compressed video. This approach is derived from [1], It relies mainly on improving the visual quality by eliminating the perceivable distortion that exists in the fingerprinted copies as well as the copies after collusion attacks produced by using unbounded Gaussian fingerprints. In order to remove this perceptual distortion, in this paper, we propose a content adaptive watermaking method which takes full advantage of both Intra and Inter frames information of the video content. Linear and non linear collusion attacks are performed to show the robustness of the proposed technique against collusion attacks while maintaining visual quality unchanged.

Wireless Sensor Networks (WSNs) may be deployed in hostile or inaccessible environments and are often unattended. In these conditions securing a WSN against malicious attacks is a particular challenge. This paper proposes to use formal methods to investigate the security of the INSENS protocol, in respect of its capability to withstand several denial of service attacks. The paper is an extension to our previous work where we proposed a formal framework to verify some wireless routing protocols. We have confirmed that the bidirectional verification employed by INSENS prevents attacks such as hello flood. However, INSENS is shown to be vulnerable to invisible node, wormhole and black hole attacks, even in a network of only a few nodes communicating over ideal channels. Packet loss in the presence of these attacks has been demonstrated and quantified using the TOSSIM wireless simulator.

Biometric system has been actively emerging in various industries for the past few years, and it is continuing to roll to provide higher security features for access control system. Many types of unimodal biometric systems have been developed. However, these systems are only capable to provide low to middle range of security feature. Thus, for higher security feature, the combination of two or more unimodal biometrics (multiple modalities) is required. In this paper, we propose a multimodal biometric system for person recognition using hand images and by integrating two different modalities palmprint and Finger-Knuckle-Print (FKP). Addressing this problem we propose an efficient matching algorithm based on Phase-Correlation Function (PCF) and using the two biometric modalities the palmprint and the FKP. The two modalities are combined and the fusion is applied at the matching-score level. The experimental results showed that the designed system achieves an excellent recognition rate and provide more security than unimodal biometric-based system.

Arabic writer identification is a very active research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in Arabic writer identification. This competition was hosted by Kaggle, it has attracted thirty participants from both academia and industry. This paper gives details on this competition, including the evaluation procedure, description of participating methods and their performances.

The cursive and ligature nature of the Arabic script make the segmentation of words into individual characters a difficult task. Despite attempts to apply methods for cursive Latin and other scripts to Arabic script, it is generally insufficient to segment the Arabic text. This paper proposes a new segmentation algorithm for the handwritten Arabic text and the main idea consists of segmenting the word into sub-words and then computing the baseline of each sub-word. Using the descenders of sub-words and the baseline, candidate points are then calculated using a vertical projection. The algorithm has been tested using 800 handwritten Arabic words taken from the IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.

Linear Regression Classifier (LRC) is state-of-the-art face recognition method that represent a probe image as a linear combination of class specific models. However, this method views the image as a point in a feature space, and thus LRC cannot accommodate severe luminance alterations. Histogram-based features, such as Multiscale Local Phase Quantisation histogram (MLPQH) have gained reputation as powerful and attractive texture descriptors showing excellent results in terms of accuracy and computational complexity in face recognition. In this paper, MLPQH features are integrated with "face" features to confront the illumination problem in LRC. The main novelty is the fusion of histogram and face features using z-score normalisation and LRC classifier. The proposed system is evaluated on two benchmarks: ORL and Extended Yale B. The results indicate a significant increase in the performance when compared with state-of the-art face recognition methods.

Biometric systems based on a single source of information suffer from limitations such as the lack of uniqueness, non-universality of the chosen biometric trait, noisy data and spoof attacks. Multibiometrics are relatively new systems that overcome those problems. These systems fuse information from multiple biometric sources in order to achieve better identification performance. In this paper, 2D and 3D palmprint are integrated in order to construct an efficient multibiometric identification system based on matching score level fusion. For that, the texture information is characterized by the rotation invariant VARiance measures (VAR) and compressed using the Principal Components Analysis (PCA). Subsequently, we use the Hidden Markov Model (HMM) for modeling the feature vector of each palmprint. Finally, Log-likelihood scores are used for palmprint evaluation. The proposed scheme is tested and evaluated using PolyU 2D-3D palmprint database of 250 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate.

The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

Detection of human skin in colored images has always been performed in known standard color spaces. In this paper a new color space coordinate is proposed based on popular existing color spaces but taking into account the most representative ones. Selection of the best color components is based on the use of the mutual information and maximum relevance minimum redundancy technique. A Gaussian model based classifier is used to test the performance of the proposed color space transformation.

Palmprint trait is emerging as a new and practical biometric solution and a few systems using full palmprints have been proposed. However, partial palmprints left at scenes of crime have not yet been exploited to recognize and find crime suspects. In this paper, a modified well known algorithm, referred to as modified phase only correlation (MPOC) is proposed to match poor quality (degraded) partial palmprints left by suspects at crime scenes against a full palmprint registered in a template database. Instead of using the conventional peak as the matching score between two palmprint images, the ratio of the conventional peak to the highest peak in the outside-lobe correlation plane is employed. Experiments were conducted on a generated database of 800 partial palmprints having an area less than 25% of that of the full palmprint images from which blurred and degraded images were created by adding an intense synthetic Gaussian noise. The results show that the proposed method yields high recognition performance thus demonstrating its ability to tackle the problem of identifying low quality partial palmprint images giving an equal error rate (EER) less than 0.4%.

In this paper, we suggest a solution for the problem of scene-of-crime shoeprints retrieval based on the use of multi-scale Harris points, which are a set of very distinctive points of interest in an image, combined with SIFT descriptor. We show that such combination can overcome the issue of retrieval of partial prints in the presence of scale and rotation distortions with Gaussian noise perturbation. Excellent results were obtained for synthetic scene images, clearly outperforming published results in the literature.

The MPEG transport stream is an extremely complex structure using interlinked tables and coded identifiers to separate the programs and the elementary streams. Quality management is therefore a complicated issue and the need to identify the degree of coding degradations in terms of coding and/or transmission errors or system failures is becoming an important criterion for the evaluation of the quality of the MPEG streams. A theoretical decoder (T-STD) defines the verification process based on the proper fill level of an MPEG decoder buffers whose size is defined by the standards in order to obtain an evaluation of the MPEG stream quality. This paper describes a new embedded and programmable solution capable of analysing MPEG streams in real time. The proposed hardware architecture provides a real time continuous buffer analysis of the MPEG stream components and is composed of several modules allowing for simultaneous modeling of the various buffers of the T-STD components (video, audio or system). Real time errors flags are generated when the buffers filling level becomes illegal (overflow,emptybuffer,transferdelay, etc.). The architecture has been modeled, validated and simulated using the SystemC and VHDL languages in combination with real MPEG DVB-T streams. A VHDL synthesisable model of our architecture allows an implementation on an field-programmable gate array circuit based on Altera APEX20K1000. The hardware implementation of this configurable T-STD allows a data rate of 232 Mbps and requires only 9738 logical cells and 4,7 kB memory.

This paper presents a new approach for palmprint recognition using a combined Fisher linear discriminant (FLD) and Gabor Wavelet responses. Gabor wavelets have properties of being more robust to image illuminations, small translations, limited rotations and having a superior feature representation in both spatial and frequency domains. On the other hand, FLD seeks those projections that are efficient for data discrimination and produces well separated classes in low-dimensional subspaces. The new combined method involves convolving a palmprint image with a series of Gabor wavelets at different scales and rotations before extracting features from the resulting Gabor filtered images. Linear discriminant analysis is then applied to the feature vectors for dimension reduction as well as class separability. Experiments show that the proposed method yields a high classification rate even when using a simple classifier when compared with other popular approaches reported in the literature.

Segmentation is one of the first and most integral pre-processing steps for any fingerprint recognition system. It is used to identify the region of interest within an image. Performance of segmentation algorithms is dependent on the type of features and threshold values selected. In this paper we propose a robust segmentation algorithm which utilizes the strength of Harris corners for segmentation. The algorithm employs dynamic thresholding and simple binary operations to provide highly accurate segmentation of the image. Experimental results prove that the algorithm provides very accurate segmentation even for low quality images.

One of the most difficult problems in automatic shoeprint classification is the matching of partial shoeprint images. This task becomes more challenging in the presence of geometric distortions (e.g. translated and/or rotated partial prints). In this paper, we evaluate the performance of advanced correlation filters (ACFs) for the automatic classification of partial shoeprints. The optimal trade-off synthetic discriminant function (OTSDF) filter and the unconstrained OTSDF (UOTSDF) filter, in particular, were used to match partial shoeprint images with different qualities. Experimental assessment using a shoeprint image database has demonstrated the efficient classification performance of ACFs compared to other state-of-the-art methods.

This paper describes an efficient watermarking technique for use to protect fingerprint images. The rationale is to embed the watermarks into the ridges area of the fingerprint images so that the technique is inherently robust, yields imperceptible watermarks, and resists well against cropping and/or segmentation attacks. The proposed technique improves the performance of optimum multibit watermark decoding, based on the maximum likelihood scheme and the statistical properties of the host data. The technique has been applied successfully on the well-known transform domains: discrete cosine transform (DCT) and discrete wavelet transform (DWT). The statistical properties of the coefficients from the two transforms are modeled by a generalized Gaussian model, widely adopted in the literature. The results obtained are very attractive and clearly show significant improvements when compared to the conventional technique, which operates on the whole image. Also, the results suggest that the segmentation (cropping) attack does not affect the performance of the proposed technique, which also provides more robustness against other common attacks.

This letter proposes an efficient extension of the set partitioning embedded block (SPECK) algorithm to lossless multispectral image coding. Such a wavelet-based coder is widely referred to in the literature, especially for lossless image coding, and is considered to be one of the most efficient techniques exhibiting very low computational complexity when compared with other state-of-the-art coders. The modification proposed in this letter is simple and provides significant improvement over the conventional SPECK. The key idea is to join each group of two consecutive wavelet-transformed spectral bands during the SPECK coding since they show high similarities with respect to insignificant sets at the same locations. Simulation results, carried out on a number of test images, demonstrate that this grouping procedure considerably saves on the bit budget for encoding the multispectral images.

In this paper, the compression of multispectral images is addressed. Such 3-D data are characterized by a high correlation across the spectral components. The efficiency of the state-of-the-art wavelet-based coder 3-D SPIHT is considered. Although the 3-D SPIHT algorithm provides the obvious way to process a multispectral image as a volumetric block and, consequently, maintain the attractive properties exhibited in 2-D (excellent performance, low complexity, and embeddedness of the bit-stream), its 3-D trees structure is shown to be not adequately suited for 3-D wavelet transformed (DWT) multispectral images. The fact that each parent has eight children in the 3-D structure considerably increases the list of insignificant sets (LIS) and the list of insignificant pixels (LIP) since the partitioning of any set produces eight subsets which will be processed similarly during the sorting pass. Thus, a significant portion from the overall bit-budget is wastedly spent to sort insignificant information. Through an investigation based on results analysis, we demonstrate that a straightforward 2-D SPIHT technique, when suitably adjusted to maintain the rate scalability and carried out in the 3-D DWT domain, overcomes this weakness. In addition, a new SPIHT-based scalable multispectral image compression algorithm is used in the initial iterations to exploit the redundancies within each group of two consecutive spectral bands. Numerical experiments on a number of multispectral images have shown that the proposed scheme provides significant improvements over related works.

This paper deals with the retrieval of scene-of-crime (or scene) shoeprint images from a reference database of shoeprint images by using a new local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale adapted Harris detector, which is an extension to multi-scale domains of the Harris corner detector; (2) automatic scale selection by the characteristic scale of a local structure. (3) SIFT (Scale-Invariant Feature Transform), one of the most widely investigated descriptors in recent years. Like most of other local feature representations, the proposed approach can also be divided into two stages: (i) a set of distinctive local features are selected by first detecting scale adaptive Harris corners where each of them is associated with a scale factor, and then selecting as the final result only those corners whose scale matches the scale of blob-like structures around them. Here, the scale of a blob-like structure is detected by the Laplace-based scale selection, (ii). for each feature detected, an enhanced SIFT descriptor is computed to represent this feature. Our improvements lead two novel methods which we call the Modified Harris-Laplace (MHL) detector, and the enhanced SIFT descriptor. In this paper, we demonstrate the application of the proposed scheme to the shoeprint image retrieval problem using six sets of synthetic scene images, 50 images for each, and a database of 500 reference shoeprint images. The retrieval performance of the proposed approach is significantly better, in terms of cumulative matching score, than the existing methods investigated in this application area, such as edge directional histogram, power spectral distribution, and pattern & topological spectra.

This paper deals with the retrieval of scene-of-crime (or scene) shoeprint images from a reference database of shoeprint images by using a new local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale adapted Harris detector, which is an extension to multi-scale domains of the Harris corner detector; (2) automatic scale selection by the characteristic scale of a local structure. (3) SIFT (Scale-Invariant Feature Transform), one of the most widely investigated descriptors in recent years. Like most of other local feature representations, the proposed approach can also be divided into two stages: (i) a set of distinctive local features are selected by first detecting scale adaptive Harris corners where each of them is associated with a scale factor, and then selecting as the final result only those corners whose scale matches the scale of blob-like structures around them. Here, the scale of a blob-like structure is detected by the Laplace-based scale selection. (ii). for each feature detected, an enhanced SIFT descriptor is computed to represent this feature. Our improvements lead two novel methods which we call the Modified Harris-Laplace (MHL) detector, and the enhanced SIFT descriptor. In this paper, we demonstrate the application of the proposed scheme to the shoeprint image retrieval problem using six sets of synthetic scene images, 50 images for each, and a database of 500 reference shoeprint images. The retrieval performance of the proposed approach is significantly better, in terms of cumulative matching score, than the existing methods used in this application area, such as edge directional histogram, power spectral distribution, and pattern & topological spectra.

In this paper, a method for automatically recognizing partial shoeprint images for use in forensic science is presented. The technique uses the phase-only correlation (POC) for shoeprints matching. The main advantage of this method is its capability to match low quality shoeprint images accurately and efficiently. In order to achieve superior performance, the use of a spectral weighting function is also proposed. Experiments were conducted on a database of images of 100 different shoes available on the market. For experimental evaluation, test images including different perturbations such as noise addition, blurring and textured background addition were generated. Results have shown that the proposed method is very practical and provides high performance when processing low quality partial-prints. The use of a weighting function provides an improvement in the recognition rate in particularly difficult cases.

Iris recognition is one of the most reliable personal identification methods and is becoming the most promising technique for high security. In this paper, we propose an efficient method for personal iris identification by investigating iris textures that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform (wavelet maxima components), and moment invariants. The proposed scheme is invariant to translation, rotation, and scale changes. Experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner.

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually "any time and any where" over the Internet. In this paper, we propose a novel framework for robust perceptual hashing of audio content using balanced multiwavelets (BMW). The framework for generating robust perceptual hash values (or fingerprints) is described. The generated hash values are used for identifying, searching, and retrieving audio content from large audio databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations

This paper discusses the suitability of reconfigurable computing to speedup classification problems using Nearest Neighbour (1NN) classifier. 1NN classifier is widely used in the literature especially in real-time applications such as face recognition, on-line hand-written character recognition and medical applications where the performance enhancement in terms of speed is desirable. To evaluate the effectivness of our implementation on Field Programmable Gate Arravs (FPGAs), experiments were carried out on two medical data sets. Results have shown that the classification accuracy is exactly same for both FPGAs and microprocessor (µP) based solutions with FPGA has superior speed performances.

In this paper, human skin detection is performed using a new color space coordinate and a Markov random field based approach. The proposed color space uses a variant of the principal component analysis technique to reduce the number of color components. The MRF model takes into account the spatial relations within the image that are included in the labeling process through statistical dependence among neighboring pixels. Since only two classes are considered the Ising model is used to perform the skin/non-skin classification process.

Following a recently introduced perceptual model for balanced multiwavelets, we outline, in this paper, an extension of our previous work and propose a new perceptual model for scalar wavelets. The proposed model is derived using multiresolution domain extensions of our previous scheme. Unlike existing models, the proposed one depends only on the image activity and not the filter sets used by the transform. The perceptual redundancy, present in the image, is efficiently quantified through a just-noticeable distortion (JND) profile. In this model, a visibility threshold of distortion is assigned to each wavelet subband coefficient Therefore, perceptually insignificant subband components can be clearly discriminated from perceptually significant ones. For instance, this discrimination can be constructively used to achieve the imperceptibility requirement often encountered in watermarking and data hiding applications. Furthermore, we illustrate, through simulation, the ability of the proposed model to efficiently capture the salient features of the underlying image regardless of the wavelet filters being used.

This paper presents an investigation on the optimum detection of multiplicative watermarks based on statistical behaviour of image contents in the transform domain. In recent works, the problem of watermark detection is viewed as a binary decision where the observation is the possibly watermarked transformed coefficients. Indeed, the detector verifies whether the watermark presented to its input is actually embedded in the input image (hypothesis HI) or not (hypothesis HQ). Such a detection scheme relies on the Neyman-Pearson criterion to derive a decision threshold by minimising the probability of missed detection with respect to a given probability of false alarm. Previous works approximate the probability density function (pdf) of the observation when hypothesis HQ holds by the pdf when hypothesis of having no watermark embedded in the input image is in force by assuming that the watermark strength is weak to some extent. However, the weakness of the watermark does fulfil the requirement on the robustness. Moreover, from the viewpoint of the decision theory, the smaller the embedding depth, the worse the watermark detection. This paper describes the drawback behind this approximation in the general case and proposes an efficient solution closer to the theoretical derivation. To validate the proposed technique, we consider a special case in which the Laplace statistical model is used.

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually “any time and any where” over the Internet. In this paper, we propose a novel framework for musical content fingerprinting using balanced multiwavelets (BMW). The framework for generating robust perceptual fingerprint (or hash) values is described. The generated fingerprints are used for identifying, searching, and retrieving audio content from large digital music databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent musical content and withstand several signal processing attacks and manipulations.

Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.

This paper proposes an adaptive blind image watermarking technique based on wavelet transform using a random sequence of real numbers. First, the image is decomposed into non overlapping blocks. Then, each block is classified as uniform or non uniform by using a JND-based classifier. The strength of the embedded watermark into the high subband coefficients of each transformed block depends upon the nature of the block according to its classification. The Neyman-Pearson criterion is used to derive the detection rule. Unlike the embedding process, the detection does not require any classification of blocs. This adaptive approach has been assessed on various standard images and compared with a similar watermarking technique (H. Inoue et al., 1999). The results have shown an obvious improvement in terms of robustness against different manipulations and a better ability of detection

In this paper, we describe a two-step variance-adaptive method for image denoising based on a statistical model of the coefficients of balanced multiwavelet transform. The model is derived in a statistical framework from a recent successful scheme developed in the seemingly unrelated front of lossy image compression. Clusters of multiwavelet coefficients are modeled as zero-mean Gaussian random variables with high local correlation. In the adopted framework, we use marginal prior distribution on the variances of the multiwavelet coefficients. Then, estimates of the local variances are used to restore the noisy multiwavelet coefficients based on a minimum mean square error estimation (MMSE) procedure. Experimental results, using images contaminated with additive white Gaussian noise, show that the proposed method outperforms most of the denoising schemes reported in the literature. In this paper, the performance comparison is restricted to non-redundant multiresolution representations.

The emergence of digital multimedia and the proliferation of its use have raised major concerns about the protection of intellectual property. In response to these concerns, digital watermarks have emerged as a possible solution for protecting the intellectual property of digital content. In this paper, we derive estimates of data-hiding capacity of balanced multiwavelet transforms. This class of transforms, relatively new, has useful properties for image processing applications as shown in this paper. Furthermore, we investigate the relevance of two closely related statistical models, developed for scalar wavelets, for modeling the statistics of balanced multiwavelet transform coefficients. Finally, we present performance results of a spread spectrum watermarking system that is based on this new transform.

In this work, new structures that implement RSA cryptographic algorithm are presented. These structures are built upon a modified Montgomery modular multiplier, where the operations of multiplication and modular reductions are carried out in parallel rather than interleaved as in the traditional Montgomery multiplier. The global broadcast of data lines is avoided by interleaving two or more encryption/decryption operations onto the same structure, thus making the implementation systolic and scalable. The digit approach has been adopted in This work. This methodology is based on varying the digit size and the level of pipelining of the structures. This parameterised approach presents the designer with an efficient way of choosing the architecture that suits better his/her requirements in terms of speed and area usage, an issue of critical importance to the resources-limited FPGA chips. The results of implementation using FPGA have shown that the proposed RSA structures outperformed those structures built around the traditional Montgomery multiplier in terms of speed, thanks to avoiding global lines broadcast.

We propose an efficient extension of set partitioning in hierarchical trees (SPIHT) for very low bit-rate wavelet based color image coding. Since the chrominance components I and Q in the YIQ format are sufficiently less significant in terms of energy compared to the luminance component, the trees within each chrominance plane are joined together in the list of insignificant sets (LIS) according to a virtual relationship parent-descendants specific to the chrominance components. For generating a fully embedded bit stream similar to SPIHT, the proposed method improves the performance of the color SPIHT based scheme, especially for very low bit rate.

This paper addresses the problem of automatic segmentation of nuclei in histopathological images. A novel method, inspired from active contour models is proposed. An evolutionary based approach, which guarantees convergence to global minimum energies has been used to solve the combinatorial optimization problem of snakes. The computational complexity, often associated with evolutionary approaches, has been reduced by short cutting the natural evolution step by means of replacing standard mutation with an oriented stochastic mutation process. Results have shown the efficiency of this method both in terms of accuracy and fast computation.

Grey level co-occurrence matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of reconfigurable hardware to accelerate the calculation of GLCM and Haralick texture features. The performances of the proposed co-processor are then assessed and compared against a microprocessor based solution.

This paper presents an efficient systolic array for the computation of the Singular Value Decomposition (SVD). The proposed architecture is three times more efficient and faster than the Brent, Luk, Van Loan (BLV) SVD systolic array. The architecture has been implemented efficiently on FPGA using a high level language for hardware design "Handel-C".

3D graphics performance is increasing faster than any other computing application. Almost all PC systems now include 3D graphics accelerators for games, Computer Aided Design (CAD) or visualization applications. This paper investigates the suitability of Field Programmable Gate Array (FPGA) devices as a low cost solution for implementing 3D affine transformations. A proposed solution based on processing large matrix multiplication has been implemented, for large 3D models, on the RC1000-PP Celoxica board based development platform using Handel-C, a C-like language supporting parallelism, flexible data size and compilation of high-level programs directly into FPGA hardware.

Matrix multiplication is very important in many types of applications including image and signal processing. This paper presents an investigation into the design and implementation of matrix product algorithm using different design approaches such as Handel-C and VHDL, where the performance of both programming languages have been presented. Solutions for processing large matrix products based partitioning methodology have been described. The proposed system has been implemented and verified using the RC1000-PP Celoxica board based development platform.

Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a High Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequency-domain image filtering application.

The two-dimensional new Mersenne number transform (2-D NMNT) was proposed for the calculation of error-free 2-D convolutions and correlations for 2-D image processing applications. The aim of this paper is to develop the radix-4/spl times/4 (VR-4/spl times/4) for fast computation of the 2-D NMNT. The new 2-D algorithm is developed and its arithmetic complexity is also analysed and compared to existing 2-D NMNT algorithms.

The paper is concerned with the development of an unsupervised segmentation algorithm for multispectral images. Due to the high dimensionality of these images, the underlining motivation of this work is on how to build up a robust unsupervised segmentation algorithm with acceptable computational complexity. After an initial approximate segmentation using the EM algorithm, a cost function associated to each pixel is proposed. This function includes a term that measures how close the pixel at hand is to the region's distribution centroids, and another term that measures the local homogeneity in the pixel's neighborhood. In addition, an edge progression technique is used to re-label pixels optimally. Extensive experiments have been carried out on many multispectral images and quantitative results have shown the efficiency of the approach.

This paper describes two approaches suitable for an FPGA implementation of Walsh-Hadamard transforms. These transforms are important in many signal processing applications including speech compression, filtering and coding. Two novel architectures for the fast Hadamard transforms using both systolic architecture and distributed arithmetic techniques are presented. The first approach uses the Baugh-Wooley multiplication algorithm for a systolic architecture implementation. The second approach is based on both distributed arithmetic ROM and accumulator structure, and a sparse matrix factorisation technique. Implementations of the algorithms on a Xilinx FPGA board are described. Distributed arithmetic approach exhibits better performances when compared with the systolic architecture approach.

Although FPGA technology offers the potential of designing high performance systems at low cost for a wide range of applications, its programming model is prohibitively low level requiring either a dedicated FPGA-experienced programmer or basic digital design knowledge. To allow a signal/image processing end-user to benefit from this kind of device, the level of design abstraction needs to be raised, even beyond a hardware description language level (e.g. VHDL). This approach will help the application developer to focus on signal/image processing algorithms rather than on low-level designs and implementations. This paper aims to present a framework for an FPGA-based coprocessor dedicated to discrete wavelet transforms (DWT). The proposed approach will help the end-user to generate FPGA configurations for DWT at the highest level without any knowledge of the low-level design styles and architectures.

Image texture segmentation is an important problem and occurs frequently in many image processing applications. Although, a number of algorithms exist in the literature. Methods that rely on the use of expectation-maximisation algorithm are gaining a growing interest. The main feature of this algorithm is that it is capable of estimating the parameters of mixture distribution. This paper presents a novel unsupervised algorithm based on expectation-maximisation algorithm where the analysis is applied on vector data rather than the grey level. This is achieved by defining a likelihood function which measures how the estimated features are fitting the present data. Experimental results on images containing various synthetic and natural textures have been carried out and a comparison with existing and similar techniques has shown the superiority of the proposed method.

The most common clues left at a crime scene when a crime is committed are shoeprint impressions. These impressions are useful in the detection of criminals and the linking of crime scenes. A novel technique for use in the detection and classification of shoeprint impressions has been developed. The technique is based on fractal based feature extraction and pattern matching methods. The computerized system developed has been extensively tested on a large database of real shoeprint impressions and is robust to small variations of image orientations and/or translations.

A new bi-directional bit serial-parallel multiplication architecture is presented. The proposed structure is regular and modular, and requires nearest neighbour communication links only, which makes it more efficient for VLSI implementation. Furthermore, a judicious deployment of latches in the circuit ensures that the multiplier operates on two coefficients of the multiplicand at the same time thus speeding up the process. Comparison of the new multiplier structure with previous ones has shown the superiority of the new architecture.

Digit-serial architectures obtained using traditional unfolding and folding techniques cannot be pipelined beyond a certain level because of the presence of feedback loops. In this paper, a novel approach for the design of pipelined digit serial-parallel multipliers is presented.