Cataract remains one of the leading causes of blindness and visual impairment worldwide, representing a significant public health concern. Anterior segment optical coherence tomography (AS-OCT) provides high-resolution visualization of ocular structures and has become a key imaging modality for nuclear cataract (NC) grading. However, existing convolutional neural network (CNN)-based methods often struggle to differentiate subtle variations between adjacent severity levels due to limited capacity for capturing long-range dependencies, thereby affecting classification accuracy. To address this challenge, we propose an automatic nuclear cataract grading network based on the Mamba architecture. This framework combines the local feature extraction capabilities of traditional CNNs with the long-range dependency modeling power of Mamba modules. Furthermore, we introduce a Hybrid Wavelet Feature Refinement Module (HWFRM), which employs wavelet transforms to extract multi-frequency representations. Integrated with a detail-guided enhancement mechanism, the module adaptively strengthens discriminative features. Channel and spatial attention mechanisms are applied to each wavelet sub-band, enabling the network to selectively emphasize important frequency components and remain sensitive to both structural and fine-detail cues. Finally, an ordinal regression loss is incorporated to explicitly model the progressive nature of cataract severity, improving the network’s ability to reduce misclassifications between adjacent categories. Extensive experiments on both a local AS-OCT dataset and a public benchmark demonstrate that our approach achieves state-of-the-art performance.

Accurate segmentation of key anatomical structures in anterior segment OCT (AS-OCT) images is critical for diagnosing serious ophthalmic conditions such as keratitis and cataract. However, due to the scarcity of labeled data in this domain, most existing methods struggle to precisely segment both the lens and the anterior chamber angle simultaneously. To address these limitations, we propose a semi-supervised segmentation framework based on collaborative training between U-Net and Mamba-UNet. A Scale Fusion Module (SFM) is introduced to integrate the outputs of both models, generating multi-scale predictions and fused pseudo-labels. A multi-scale supervision strategy is then employed to guide learning at different levels. Additionally, we design a novel anatomical structure consistency loss that leverages anatomical properties from the fused pseudo-labels to preserve anatomical correctness. Experimental results on two AS-OCT datasets demonstrate the effectiveness and superiority of our proposed approach.

Medical image translation enables the generation of contrast-enhanced CT (CECT) images from non-contrast CT (NCCT) scans, reducing dependence on iodinated contrast agents (ICAs) and minimizing associated health risks. Although diffusion models outperform generative adversarial networks (GANs) in medical image translation, challenges remain in improving sampling speed and restoring anatomical details. To address these issues, we propose a Decoupled Diffusion Model (DDM). Specifically, we first decouple the input image into high-frequency and low-frequency components via discrete wavelet transform (DWT) to enable parallel computing for accelerated sampling. Furthermore, we design a Wavelet UNet (WUNet) to enhance the recovery of anatomical details by leveraging the multi-scale representation capabilities of wavelet transform. Extensive experiments on two clinical datasets, TAP-CT and Coltea-Lung-CT-100W, demonstrate the superior performance of our method, indicating its potential for real-world clinical translation.

Accurate structural segmentation and landmark detection in anterior segment optical coherence tomography (AS-OCT) images are crucial for extracting clinical parameters that guide the diagnosis and treatment of diseases such as glaucoma. However, current mainstream algorithmic paradigms suffer from an inherent limitation: their performance improvements heavily rely on large amounts of high-quality annotations. To overcome this bottleneck, we propose a novel semi-supervised multi-task learning framework. Our framework first incorporates the powerful Segment Anything Model (SAM) image encoder to enhance the model’s general feature extraction capability. To address SAM’s adaptability issues in the medical imaging domain, we design an adaptive feature fusion adapter (AFFA) for targeted fine-tuning, thereby improving its performance on AS-OCT images. Simultaneously, our proposed synergistic feature exchange module (SFEM) enables mutual promotion between the segmentation and detection tasks. Experimental results on a local dataset demonstrate that our proposed method achieves superior performance.

Segmenting intricate curve structures like retinal blood vessels, encompassing both fine and coarse details, remains a significant challenge. This work proposes a novel module that divides these complex curve structures into trunks and branches, fuses the input as auxiliary information, and optimizes the breakpoints of different curve parts through losses. In order to balance the redundant information that may lead to model overfitting, a unique feature perturbation strategy is introduced after the backbone decoding process to enhance the model’s robustness to complex curve structure segmentation tasks. Experiments show that this method can effectively distinguish different topological structures of blood vessels and maintain high segmentation accuracy even at blood vessel intersections or breakpoints, which holds immense potential for diverse future applications in image segmentation.

In the segmentation of curve structures, the discontinuity may lead to an incomplete topological representation of the structure. The current methods for reconnecting curve structures lack physical explanations and are limited in their effectiveness in image processing. In order to address these constraints, a new algorithm for reconnecting curve structures in segmentation is proposed by combining fluid mechanics principles, especially Bernoulli’s principle and random walks. This algorithm calculates the similarity of fracture curves to find the fracture curve.Redefined the energy calculation method during the reconnection process and calculated the probability of energy transfer. It adopts a biased random walk guided by the energy transfer probability in the graph until the walker reaches the target. This innovative approach provides a more comprehensive physical explanation and improves the effectiveness of image processing.

Choroidal neovascularization (CNV) is a prevalent retinal disease that can result in vision loss and blindness. Therefore, accurate segmentation of CNV is crucial for ophthalmologists to effectively treat patients with CNV. However, due to the complex pathological features of CNV, there exist significant variations in the size and shape of different CNV lesions. As a result, the challenge of segmenting CNV in optical coherence tomography (OCT) images remains unresolved. In this paper, we propose a Graph Convolutional Network with Feature Enhancement (GCFE-Net) for CNV segmentation. Our approach introduces a Graph Attention Module (GAM) on top of the encoder to extract pixel characteristics and enhance the model's space utilization. Additionally, we propose a Dynamic Fusion Module (DFM) in the decoder to address the issue of semantic misalignment when the CNV scale undergoes substantial changes. The effectiveness of the proposed method is demonstrated through experiments conducted on the Cell public dataset.

Optical coherence tomography (OCT) is a widely used ophthalmic imaging technique, and accurate detection of retinal biomarkers in OCT images can help physicians diagnose diseases. However, OCT images are not easy to obtain and are time-consuming and laborious. In addition, the size of biomarkers varies widely. Past deep learning-based methods can hardly solve the above problems well. Thus, to overcome the above challenges, we propose a weakly semi-supervised method called PO-Net for the detection of retinal biomarkers in OCT images. In the proposed method, we utilize a small set containing images with bounding box labels and a large set of weakly annotated images with only one point annotation per biomarker. The training of the net is composed of several steps. In the first step, we use the weakly annotated images to train a point-to-box regression network. In the second step, the point-annotated images are used to generate pseudo-bounding boxes. In the third step, the images with bounding box annotations and the generated images with pseudo-bounding box labels are used as inputs to the detection network. Furthermore, we propose a multi-scale feature fusion module to deal with the problem of biomarker appearance changes. The effectiveness of the proposed method is evaluated on a local dataset, and the state-of-the-art performance of our method is achieved in all datasets with different percentages of bounding box annotations.

Optical coherence tomography angiography (OCTA) technology has been applied to retinal examination for clinical diagnosis. OCTA images reveal important details of eye diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). DR and AMD are the leading causes of blindness in these diseases. Quantitative analysis of retinal vessel can help doctors diagnose retinal diseases and track the progression of these diseases. In this paper, we propose a new OCTA vessel segmentation framework based on variable vessel thickness. Specifically, to guide the network to adapt to the scale changes of vessels, we construct a vessel structure attention module. It can better capture the vessel structure by guiding the network to pay attention to the vessel edge information and help the network to establish a good context dependency. By assigning corresponding weights to different pixels, it helps the network to better learn vessels of different thicknesses and segment a more complete vessel structure. Finally, the framework is evaluated on the OCTA500 dataset, and experimental results demonstrate the effectiveness of the proposed segmentation framework.

Optical Coherence Tomography Angiography (OCTA) is a rapid, non-invasive imaging technique, which can display the vascular system in detail. Retinal vascular segmentation on OCTA images is of great significance for the diagnosis and treatment of many vision-related diseases. However, there is still much room for improvement in the research of retinal vascular segmentation due to the low visibility of vascular edges and high vascular complexity. Therefore, we propose a novel OCTA vascular segmentation network (VCT-Net). The network is a U-shaped network consisting of a transformer branch and a convolution branch. The structure enables the network to make full use of global and local information. The transformer branch uses a swin transformer to reduce computational complexity. Experimental results show that VCT-Net achieves better vascular segmentation performance than other deep learning methods on OCTA-6M dataset.

Identifying biomarkers from optical coherence tomography images is critical in diagnosing and treating ophthalmic diseases. Most existing biomarker segmentation methods require pixel-level annotations for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised biomarker localization and segmentation method. The framework includes a classification network and a teacher-student network to exploit category annotated data through contrastive learning and anomaly localization strategies based on knowledge distillation. The classification network combines cross-entropy loss and self- supervised contrastive loss to ensure that the model focuses on the characteristics of the biomarker of interest. We introduce a knowledge distillation-based anomaly localization method to localize biomarker-related pathological regions accurately. The trained classification network acts as a teacher model to guide the training of the student network to learn the features of normal OCT images. The biomarker regions can be accurately localized by the differences between the feature maps generated by the two networks. Experiment results on the public dataset demonstrate the effectiveness of the proposed method.

Optical coherence tomography (OCT) is an important ophthalmic imaging technique, which can generate high-resolution anatomical images and plays an important role in the detection of retinal biomarkers. However, the appearance of retinal biomarkers is complex, and some of these biomarkers differ greatly among different categories, while many features are similar. In addition, the boundaries of retinal biomarkers are often indistinguishable from the background. In this study, we propose a self-supervised contrastive boundary consistency network (SCB-Net) to detect retinal biomarkers in OCT images. A self-supervised contrastive classification module is proposed to improve the classification ability of the network between different categories of retinal biomarkers. Furthermore, in order to make the boundary of the retinal biomarkers located by the network closer to the ground truth, the boundary consistency is added on the basis of the original regressor to jointly constrain the boundary localization. The experimental results on a local dataset show that our proposed SCB-Net method achieves good detection performance compared with other detection methods.

Infrared imaging is currently the most effective clinical method to evaluate the morphology of the meibomian glands (MGs) in patients. As an important indicator for monitoring the development of MG dysfunction, it is necessary to accurately measure gland-drop and gland morphology. Although there are existing methods for automatic segmentation of MGs using deep learning frameworks, they require fully annotated ground-truth labels for training, which is time-consuming and laborious. In this article, we proposed a new scribble-supervised deep learning framework for segmenting the MGs, which only requires easily attainable scribble annotations for training. To cope with the shortage of supervision and regularize the network, a transformation consistent strategy is incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed segmentation method consists of two stages. In the first stage, a U-Net network is used to obtain the meibomian region segmentation map. In the second stage, we concentrate on segmenting glands in the meibomian region. We utilize the gradient prior information of the original image at the decoder part of the segmentation network, which can coarsely locate the target contour. We automatically generate reliable labels using the exponential moving average of the predictions during training and filter out the unreliable pseudo-label by uncertainty threshold. Experimental results on a local MG dataset and two other public medical image datasets demonstrate the effectiveness of the proposed segmentation framework.

When optical coherence tomography (OCT) is used for retinal disease diagnosis, it is critical to detect and classify the biomarkers from the OCT B-scans of patients. In this paper, we propose a novel weakly supervised approach that utilizes healthy data and image-level labels for biomarker detection and classification. The proposed approach is based on a hybrid network which integrates adversarial generative network and guided attention into one framework. The framework includes an anomaly detection network and a classification network. The anomaly detection network reconstructs an input image with biomarkers to a reconstructed image and the reconstructed image is compared with the input image to locate the biomarkers. Inspired by the guided attention inference network, we utilize the discriminator trained in the anomaly detection network as a classifier twice to reduce model parameters and obtain a complete attention map with class information to get biomarker classes. Experimental results with a large dataset demonstrate the effectiveness of the proposed detection and classification framework.

Near-infrared imaging is currently the most effective clinical method for evaluating the morphology of the meibomian glands in patients. Meibomian gland dysfunction (MGD) is a chronic and diffuse disease of the meibomian glands, which is an important cause of eye diseases such as dry-eye and blepharitis. Therefore, it is important to monitor the gland-drop and gland morphology for MGD patients. In this paper, we proposed a new scribble-supervised deep learning method for segmenting the meibomian glands. The proposed segmentation network consists of two stages. The first stage uses the U-Net network to obtain the meibomian region segmentation map. The second stage focuses on the meibomian region, combining spatial attention, gradient map and label filtering to generate the meibomian gland segmentation results. Experimental results on a local meibomian gland dataset demonstrate the effectiveness of the proposed segmentation framework.

Macular disease has become one of the main causes of blindness. The application of optical coherence tomography (OCT) assisting ophthalmologists to analyze them is essential in clinical diagnosis and treatment. Recently, the two-stage attention-based method was proposed to classify and segment the lesion area. However, the knowledge learned by the classification task cannot be transferred to the segmentation task in the two-stage method. In this paper, we propose a novel one-stage attention-based method for retinal OCT image classification and segmentation, simultaneously the two tasks can promote each other. Specifically, attention map obtained by Convolutional neural networks (CNN) classifiers are applied for guiding the network to achieve more accurate classification. For gaining stronger discrimination ability, we apply the priori knowledge of grayscale difference to generate pseudo ground truth for constraining the attention map. Experimental results on the UCSD dataset demonstrate the efficiency of the proposed network.

Image aesthetics quality evaluation, which allows computers to judge "beauty and ugliness", is widely used in fields of image recommendation and image editing etc.. Most aesthetic evaluation methods can only output one type of evaluation result, thus, the scope of their application scenarios is limited. To solve this problem, we proposed a multi-task based aesthetic evaluation system, which can output the image style label and three forms of the aesthetic evaluation results for a image. The training procedure is divided into two stages to realize the aesthetic evaluation tasks from coarse to fine. Experiments on AVA dataset show that it can accomplish an efficient and comprehensive image aesthetic quality evaluation.

Optical coherence tomography (OCT) imaging can obtain high-resolution cross-sectional scans of the retina, which can be used in clinical diagnosis. Changes in the thickness of layers indicate the onset of retinal diseases, motivating an accurate measurement of the thickness of retinal layers. Thus, an automatic and robust layer segmentation method is necessary. In this paper, we propose a deep learning-based multi-task framework to obtain the topologically consistent layer segmentation in OCT B-scans. By integrating the distance maps of retinal layer surfaces, the segmentation task is regarded as a multi-task problem of regression and classification. Besides, considering the multi-task learning problem, we propose a task-specific attention module to learn the task-tailored features. Experiment results on a public OCT dataset with multiple sclerosis (MS) demonstrate the effectiveness of the proposed method.

This article presents a novel evolution strategy-based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multiobjective optimization problems in dynamic environments. First, an efficient self-adaptive precision controllable mutation operator is designed for individuals to explore and exploit the decision space. Second, the simulated isotropic magnetic particles niching can guide the individuals to keep uniform distance and extent to approximate the entire Pareto front automatically. Third, the nondominated solutions (NDS) guided immigration can facilitate the population convergence with two different strategies for the NDSs and the dominated solutions, respectively. As a result, our algorithm can track the new approximate Pareto set and approximate Pareto front as quickly as possible when the environment changes. In addition, DMOES can obtain a well-converged and well-diversified Pareto front with much less population size and far lower computational cost. The larger the number of individuals, the sharper the contour of the resulted approximate Pareto front will be. Finally, the proposed algorithm is evaluated by the FDA, dMOP, UDF, and ZJZ test suites. The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art dynamic multiobjective evolutionary algorithms.

Accurate access to nuclear information on digital pathology images can assist physicians in diagnosis and subsequent treatment. The pathological images have a large number of nuclei and part of nuclei is touching, manual segmentation is time consuming and error prone. Therefore it is an important task to develop a accurate nuclei segmentation method. For traditional methods, it is hard to obtain a accurately nuclei segmentation result, because the nuclei have many different characterizations. In this paper, we propose a new nuclei segmentation method (MDC-Net), which is a deep fully convolutional network. The network contains multiple residual operations to reduce detail loss in image. In addition, dilated convolution which has different dilation ratio is used to increase receptive field. MDC-Net contains the distance map and contour image, enhancing information on individual nuclei to get accurate segmentation results. We improve the segmentation effect by using the post-processing operate. We demonstrate that MDC-Net can obtain state-of-the-art results on public dataset with multiple organ slices compared with other popular methods.

Medical images from different modalities (e.g. MRI, CT) or contrasts (e.g. T1, T2) are usually used to extract abundant information for medical image analysis. Some modalities or contrasts may be degraded or missing, caused by artifacts or strict timing during acquisition. Thus synthesizing realistic medical images in the required domain is meaningful and helpful for clinical application. Meanwhile, due to the time-consuming of manual annotation, automatic medical image segmentation has attracted much attention. In this paper, we propose an end-to-end cross-domain synthesis and segmentation framework SSA-Net. It is based on cycle generative adversarial network (CycleGAN) for unpaired data. We introduce a gradient consistent term to refine the boundaries in synthesized images. Besides, we design a special shape consistent term to constrain the anatomical structure in synthesized images and to guide segmentation without target domian labels. In order to make the synthesis subnet focusing on some hard-to-learn regions automatically, we also introduce the attention block into the generator. On two challenging validation datasets (CHAOS and iSeg-2017), the proposed method achieves superior synthesis performance and comparable segmentation performance.

In the field of medical images, skin lesion segmentation in dermoscopic images is a challenging task due to the irregular and blurring edges of the lesion and the presence of various artifacts. With the successful application of generative antagonistic network (GAN), a new neural network for skin lesion segmentation is proposed. The encoder-decoder with Dense-Residual block is used in the segmentation network which enables the network to be trained more efficiently. A multi-scale objective loss function is introduced to utilize deep supervision. We combine Jaccard distance and End Point Error which can solve lesion-background imbalance problem in pixel-level classification for skin lesion segmentation and also alleviate the problem of boundary ambiguity. A joint loss function is finally used, which includes a multi-scale objective loss function, End Point Error and Jaccard distance content loss function. Experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017.

Optical coherence tomography (OCT) is a high-resolution and noninvasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retinal diseases. However, manual segmentation is often a time-consuming and subjective process. In this work, we propose a new method for automatically segmenting retinal OCT images, which integrates deep features and hand-designed features to train a structured random forests classifier. The deep convolutional features are learned from deep residual network. With the trained classifier, we can get the contour probability graph of each layer; finally, the shortest path is employed to achieve the final layer segmentation. The experimental results show that our method achieves good results with the mean layer contour error of 1.215 pixels, whereas that of the state of the art was 1.464 pixels, and achieves an F1-score of 0.885, which is also better than 0.863 that is obtained by the state of the art method.

Optical coherence tomography (OCT) is a primary imaging technique for ophthalmic diagnosis, which has the advantages of high-resolution and non-invasive. Diabetes is a chronic disease which might increase the risk of blindness. Hence, it is important to monitor the morphology of the retinal layer and fluid accumulation for Diabetic macular edema (DME) patients. In this paper, we proposed a new semi-supervised fully convolutional deep learning approach for segmenting retinal layers and fluid region in retinal OCT B-scans. The proposed semi -supervised approach leverages unlabeled data through an adversarial learning strategy. The segmentation framework includes a segment network and a discriminate network, both two networks are u-net like fully convolutional architecture. The objective function of the segment network is a joint loss function including multi-class cross entropy loss, adversarial loss and semi-supervise loss. Experiment result on the duke DME dataset demonstrate the effectiveness of the proposed segmentation framework.

Optical coherence tomography (OCT) is one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retinal diseases. However, manual segmentation is often a time-consuming and subjective process. A number of methods have been proposed for layer segmentation on retinal OCT images, but these methods are not suit for retinal pathological OCT images. In this work, we propose a new method for layers (two layers, inner limiting membrane, outer segments-retinal pigment epithelium) segmentation in pathological retinal OCT images using shortest path algorithm enhanced with backtracking and direction consistency. To quantitate the performance of the proposed method, we compared method to three segmentation methods. The experimental result shows that our method is more suited for retinal OCT images in pathological and achieves better result than the state-of-the-art methods.

This paper studies the automatic classification of abnormal mutant and normal fishes by analyzing their movements recorded in the video. Motivated by the observation that mutant fishes have muscle disorders so that their bodies cannot bend sufficiently to swim normally, a kinematics-driven movement change detection approach is proposed to automatically segment the recorded video into different video segments. Furthermore, a new feature extraction method, called local kinematics shape pattern (LKSP), is proposed in this paper to provide discriminative spatiotemporal kinematics measurements of fish body movements. The histogram of the proposed LKSP features is incorporated into a motion classification approach to identify whether the fish is normal or a mutant. The experiments are conducted using the real-world recorded videos to demonstrate the superior performance of the proposed approach.

The monitoring and analysis of cattle follicle dynamics plays an important role in improving the cattle pregnancy rate, both theoretically and practically. In this paper, we demonstrate a follicle monitoring system based on 3D ultrasound image. This system integrates an image de-noising algorithm, edge detection algorithm and 3D reconstruction algorithm together using the MFC framework and OpenGL technologies. Using this system, we realize the following functions: image de-noising, follicle detection, follicle surface extraction, follicle 3D reconstruction, follicle volume calculation, etc.

Blood vessel segmentation is an important problem for quantitative structure analysis of retinal images, and many diseases are related to the structure changes. Manual segmentation is time consuming and computer aided segmentation is required to deal with large amount images. This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. Multiple kernel learning (MKL) is introduced to deal with the problem, utilizing features from Hessian matrix based vesselness measure, response of multiscale Gabor filter, and multiple scale line strength features. The method is evaluated on the publicly available DRIVE and STARE databases. The performance of the MKL method is evaluated and experimental results show the high accuracy of the proposed method.

In this paper, we investigate the possibility of using limited feature points (shape landmarks) from 3-D human body shapes to recognize the gender of human beings. Several machine learning algorithms and feature extraction algorithms (principal component analysis and linear discriminant analysis) are investigated and analyzed in this paper. Experimental results on a large dataset containing 2484 3-D shape models show that limited feature points (shape landmarks) can be used for gender recognition and can achieve high recognition rate, which provides a fast gender recognition technique. The research provides a potential research direction for gender recognition.

In this paper, we describe a framework for GPS/INS based Portable Positioning System. The framework includes two main components: receiving terminal and monitoring center. In the receiving terminal, the digital compass and the GPS modules are both connected with an I/O interface in a FPGA to provide an integrated positioning solution. This integrated positioning algorithm can solve the problems arising both in standalone modes and in traditional integrated measurements. In the monitoring center, several improvements have been discussed to ensure runtime efficiency and the robustness of the PPS. The experimental results from two positioning modes validate the effectiveness of the proposed algorithm.

This paper presents a novel algorithm for vascular tree segmentation based on shortest path connection. The connected vascular tree provides topological features that are instrumental in image-aided diagnosis. The proposed method can enforce the connectivity as well as remove the false detection at same time. Multi-scale ridge detector is employed that can locate vessels with different widths. To connect the isolated ridge, the single-source shortest path algorithm is tailored that searches the optimal path. The path metric is defined in terms of probability of pixel belong to foreground and background. This mechanism enables that the false detection could be removed via hypothesis testing. The topological vascular tree with 1-pixel width and fully-connection is segmented from the retinal image. The simplicity and efficiency of the proposed method make it practical to be employed in image-aided diagnosis system readily.

In this paper, we investigate mass classification using an improved local binary pattern operator. In the proposed classification algorithm, the improved local binary pattern operator is used to extract the features of masses and is used to determine whether the mass is benign or malignant. For classifier, support vector machine is adopted. 309 images from the DDSM database were used and the experimental results show the effectiveness of the proposed algorithm.

Gender recognition has important applications in identity recognition, demographic survey, and human-computer interaction systems. In the past, gender recognition was based on 2-D images or videos, which has many limitations and disadvantages, such as low accuracy and sensitivity to the viewpoint of the camera and lighting conditions. In this paper, we investigate gender recognition using 3-D human body shapes. The 3-D human body shapes used for gender recognition were obtained by laser scanning. Different machine-learning algorithms and feature-extraction methods are investigated and analyzed in this paper. Experimental results show that the support vector machine (SVM) is the best classification algorithm, and the features represented using distributions of normals are very effective for gender recognition. Furthermore, Fourier descriptor (FD) is a robust method to analyze the breast regions and has great potential applications in 3-D human-body-shape-based biometrics. The research demonstrates that our shape-based gender recognition has achieved a very high recognition rate. The techniques provide effective ways for gender recognition and overcome some limitations in 2-D technologies.

Particle filter is a popular stochastic tracker for object tracking. In articulated human body pose tracking, lots of work focuses on increasing sampling efficiency by incorporating optimization algorithm into particle filter. In this study, we propose a modified optimization based particle filter algorithm for pose tracking. The new algorithm can maintain the diversity of particle set by using a suppression scheme. Experimental results show that the proposed method can cope with multi-modality and can obtain more accurate estimation than other optimization based particle filter methods.

With the wide applications of video cameras in surveillance, video analysis technologies have attracted the attention from the researchers in computer vision field. In video analysis, human behavior recognition and understanding is an important research direction. By recognition and understanding the human behaviors, we can predict and recognize the happening of crimes and help to the police or other agencies to react immediately. In the past, large amount of intensive papers have been published on human behavior understanding in videos. Generally speaking, the procedure of human behavior understanding can be divided into the following stages: human segmentation and tracking, and human behavior recognition. In this paper, we provide a comprehensive survey of the recent development of all these stages. We will also discuss the difficulties in behavior understanding and identify possible future directions.

Image enhancement technologies have been widely used for improving the quality of the images for screening mammograms. In this paper, we will focus on the enhancement of breast calcifications, which are the deposits of calcium that can be seen on a mammogram of the breast. In the proposed method, the original image and the normalized gradient image of the original image are first decomposed into a multi-level Laplacian pyramids, and then the features in different scales are enhanced level by level based on a contrast measure during the reconstruction stage. Because the importance of different levels is different, different weights are used in different levels. Experiments proved the effectiveness of the proposed algorithm.

Locality sensitive discriminant analysis (LSDA) algorithm is a new data analysis tool for studying the class relationship between data points, which can utilize local geometry structure of the data manifold and discriminant information at the same time. A major disadvantage of LSDA is it that can only deal with vector data, and thus is often confronted with singularity problem. In this paper, an extension of LSDA is proposed, called two-dimensional locality sensitive discriminant analysis (2DLSDA), which is directly based on 2D image matrices for face recognition, can overcome the singularity problem and utilize the spatial information among pixels more effectively. Besides, based on the Schur decomposition, the projection matrices can be obtained efficiently with high numerical stability, and orthogonality of projection matrix is guaranteed. Experiments on both ORL and Yale datasets demonstrate that the proposed method can achieve better performance than PCA, LDA and LSDA methods.

Dimension reduction is critical in many areas of pattern classification and machine learning and many discriminant analysis algorithms have been proposed. In this paper, a Pairwise Covariance-preserving Projection Method (PCPM) is proposed for dimension reduction. PCPM maximizes the class discrimination and also preserves approximately the pairwise class covariances. The optimization involved in PCPM can be solved directly by eigenvalues decomposition. Our theoretical and empirical analysis reveals the relationship between PCPM and Linear Discriminant Analysis (LDA), Sliced Average Variance Estimator (SAVE), Heteroscedastic Discriminant Analysis (HDA) and Covariance preserving Projection Method (CPM). PCPM can utilize class mean and class covariance information at the same time. Furthermore, pairwise weight scheme can be incorporated naturally with the pairwise summarization form. The proposed methods are evaluated by both synthetic and real-world datasets.

In this paper, we propose a new algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE) for face recognition. ONPE can preserve local geometry information and is based on the local linearity assumption that each data point and its k nearest neighbors lie on a linear manifold locally embedded in the image space. ONPE is based on Neighborhood Preserving Embedding (NPE), but overcomes the metric distortion problem of NPE, while metric distortion usually leads to performance degradation. Besides, we propose a classification method (ONPC) based on the ONPE, which use local label propagation method in the reduced space for face recognition. ONPC is based on the natural assumption that the local neighborhood information is also preserved in reduced space, and the label of a data point can be obtained in the reduced space by the labels of its neighbors. Experimental results on two face databases demonstrate the effectiveness of our proposed method.

This paper presents an adaptive-weighted hidden Markov models (AWHMM) method for classification of 3D models into a set of pre-determinated model classes. Two new features are proposed to capture model surface orientation information. In the method, each model class is represented by four HMMs corresponding to four type features. During the training process, for each type of feature, the feature statistics of each model class and the spatial dynamics are learned by an HMM. During the classification process, characteristics of the test model are analyzed by the HMMs corresponding to each model class. The likelihood scores provided by the HMMs are calculated, and the highest weighted sum score provides the class identification of the test model. Furthermore, with unsupervised learning, each HMM and type weight are adapted with test models, which results in better modeling over time. Based on experiments, the proposed algorithm achieves much better performance than a baseline method and better performance than HMMs using only two features with fixed weights method.

Currently available workflow management systems lack abilities for supporting interorganizational processes. In this paper, we present an approach to model interorganizational business processes based on the pi-calculus technique. This approach provides a formal foundation for processes with interorganizational interaction structures, appropriate for modeling distributed cooperative workflow systems.