Abstract Sand-dust color images suffer from poor image visibility and serious color cast that significantly affect the performance of outdoor computer vision systems. Therefore, this paper proposes an integrated enhancement method for the sand-dust image. The proposed method improves the image visibility and removes the sand-dust color cast. It integrates two main processes in two different color models. The adaptive gray world-blue channel (AGW-B) is utilized in the Red-Green-Blue (RGB) color model to remove the sand-dust color cast. Then, the contrast limited adaptive histogram equalization with normalized intensity and saturation correction (CLAHE-NISC) is conducted in a Hue-Saturation-Intensity (HSI) color model to enhance the image visibility. Sand-dust images with weak, medium, and extreme sand-dust color casts were utilized in the subjective and objective evaluations. Results show that the proposed method produced better and clearer enhanced images than the other four current sand-dust image enhancement methods.

Human action recognition has been receiving extensive interest among researchers from the computer vision community. Numerous successful action recognition techniques have demonstrated the effectiveness of learning action knowledge from still images or motion videos. The relevant action information learned for the same action via various media types, such as images or videos, may be correlated. Nonetheless, less attention has been paid to adapting the action knowledge from images to videos to enhance action recognition performance in videos. Furthermore, most existing video action recognition methods suffer from insufficient labeled training videos. Overfitting could be an issue in these circumstances; hence, action recognition performance can be inhibited. This paper proposes an adaptation framework to transfer knowledge from images to videos for action recognition. A multi-task learning framework is designed to optimize the image and video domain classifiers jointly. The general Schatten p-norm is applied to the classifiers to mine the shared knowledge between these two domains. In this way, our framework can learn the correlated action semantics by leveraging the shared components of labeled images and videos. Our proposed approach can fully use the action knowledge from images and performs better in the case of poor and limited video data compared with the existing state-of-the-art action recognition techniques.

In this study, a new fuzzy‐based technique is introduced for denoising images corrupted by impulse noise. The proposed method is based on the intuitionistic fuzzy set (IFS), in which the degree of hesitation plays an important role. The degree of hesitation of the pixels is obtained from the values of memberships of the object and the background of the image. After minimising the obtained hesitation function, the IFS is constructed and the noisy pixels are detected outside the neighbourhood of mean intensity of the object and the background of an image. Denoised images are relatively analysed with five other methods: modified decision‐based unsymmetric trimmed median filter, noise adaptive fuzzy switched median filter, adaptive fuzzy switching weighted average filter, adaptive weighted mean filter, iterative alpha trimmed mean filter. Performances of the proposed method along with these five state‐of the‐art methods are evaluated using a peak signal‐to‐noise ratio and error rate along with the time for computation. Experimentally, derived denoising method showed an improved performance than five other existing techniques in filtering noise in images due to the reduction of uncertainty while choosing the noisy pixels.

Recently, increasing attention has been directed to study and identify the emotional content of a spoken utterance. This study introduces a method to improve emotion classification performance under clean and noisy environments by combining two types of features: the proposed neural‐responses‐based features and the traditional INTERSPEECH 2010 paralinguistic emotion challenge features. The neural‐responses‐based features are represented by the responses of a computational model of the auditory system for listeners with normal hearing. The model simulates the responses of an auditory‐nerve fibre with a characteristic frequency to a speech signal. The simulated responses of the model are represented by the 2D neurogram (time‐frequency representation). The neurogram image is sub‐divided into non‐overlapped blocks and the averaged value of each block is computed. The neurogram features and the traditional emotion features are combined together to form the feature vector for each speech signal. The features are trained using support vector machines to predict the emotion of speech. The performance of the proposed method is evaluated on two well‐known databases: the eNTERFACE and Berlin emotional speech data set. The results show that the proposed method performed better when compared with the classification results obtained using neurogram and INTERSPEECH features separately.

We propose a real-time method to accurately track the human head pose in the 3-dimensional (3D) world. Using a RGB-Depth camera, a face template is reconstructed by fitting a 3D morphable face model, and the head pose is determined by registering this user-specific face template to the input depth video.

Kalman filter is normally used to enhance speech quality in a noisy environment, in which the speech signals are usually modelled as autoregressive (AR) process, and represented in the state‐space domain. It is a known fact that to identify the changing AR coefficients in every time state requires extensive computation. In this paper, the authors develop a bidirectional Kalman filter and apply it in a speech processing system. The proposed filter uses a system dynamics model that utilises the past and the future measurements to form an estimate of the system's current time state. It provides efficient recursive means to estimate the state of a process that minimises the mean of the squared error. Compared to the conventional Kalman filter, the proposed filter reduces the computation time in two ways: (i) by avoiding the computation of AR parameters in each time state, and (ii) by reducing the dimension of the matrices involved in the difference equations and the measurement equations into constant (1 × 1) matrices. The speech recognition result shows that the developed speech recognition system becomes more robust after the proposed filtering process, and the proposed filter's low computational expense makes it applicable in the practical hidden Markov model‐based speech recognition system.

A visual quality evaluation of image object segmentation as one member of the visual quality evaluation family has been studied over the years. Researchers aim at developing the objective measures that can evaluate the visual quality of object segmentation results in agreement with human quality judgments. It is also significant to construct a platform for evaluating the performance of the objective measures in order to analyze their pros and cons. In this paper, first, we present a novel subjective object segmentation visual quality database, in which a total of 255 segmentation results were evaluated by more than thirty human subjects. Then, we propose a novel full-reference objective measure for an object segmentation visual quality evaluation, which involves four human visual properties. Finally, our measure is compared with some state-of-the-art objective measures on our database. The experiment demonstrates that the proposed measure performs better in matching subjective judgments. Moreover, the database is available publicly for other researchers in the field to evaluate their measures.

In this study, the authors propose a speech recognition system using harmonic structure related information to detect harmonic features in noisy environment. The proposed algorithm first extracts the harmonic components contained inside the speech signals using sine function convolution. By setting the frequency of the sine function as equal to the fundamental frequency of speech signals, harmonic components can be extracted out. The reconstructed signal obtained by summing up the extracted harmonic components is found to have a high degree of correlation with the original signal. The extracted frame energy measure of the harmonic components has been further processed to become dynamic harmonic features and then used together with the European Telecommunications Standards Institute (ETSI) front‐end processed mel‐frequency cepstral coefficients (MFCC) feature or the perceptual linear prediction (PLP) feature in the speech recognition system. The proposed enhanced speech recognition system shows a better recognition rate over the ETSI front‐end processed MFCC (or PLP)‐based speech recognition system.

This study describes a new approach to enhance noisy speech signals using the discrete Tchebichef transform (DTT) and the discrete Krawtchouk transform (DKT). The DTT and DKT are based on well‐known orthogonal moments: the Tchebichef and Krawtchouk moments, respectively. The representations of speech signals using a limited number of moment coefficients and their behaviour in the domain of orthogonal moments are shown. The method involves removing noise from the signal using a minimum‐mean‐square error in the domain of the DTT or DKT. According to comparisons with traditional methods, the initial experiments yield promising results and show that orthogonal moments are applicable in the field of speech signal enhancement. The application of orthogonal moments could be extended to speech analysis, compression and recognition.

In this paper, a face recognition system based on Discrete Tchebichef-Krawtchouk Transform DTKT and Support Vector Machines SVMs is proposed. The objective of this paper is to present the following: (1) the mathematical and theoretical frameworks for the definition of the DTKT including transform equations that need to be addressed. (2) the DTKT features used in the classification of faces. (3) results of empirical tests that compare the representational capabilities of this transform with other types of discrete transforms such as Discrete Tchebichef transform DTT, discrete Krawtchouk Transform DKT, and Discrete Cosine transform DCT. The system is tested on a large number of faces collected from ORL and Yale face databases. Empirical results show that the proposed transform gives very good overall accuracy under clean and noisy conditions.

This study introduces a new set of orthogonal polynomials and moments and the set's application in signal and image processing. This polynomial is derived from two well-known orthogonal polynomials: the Tchebichef and Krawtchouk polynomials. This study attempts to present the following: (i) the mathematical and theoretical frameworks for the definition of this polynomial including the modelling of signals with the various analytical properties it contains, as well as, recurrence relations and transform equations that need to be addressed; and (ii) the results of empirical tests that compare the representational capabilities of this polynomial with those of the more traditional Tchebichef and Krawtchouk polynomials using speech and image signals from different databases. This study attempts to demonstrate that the proposed polynomials can be applied in the field of signal and image processing because of the promising properties of this polynomial especially in its localisation and energy compaction capabilities.

Moments extraction from high resolution images in real time may require a large amount of hardware resources. Using a direct method may involve a critically high operating frequency. This paper presents two improved digital-filter based moment accelerators, as exemplified by a Tchebichef moments computation engine, to introduce features that contribute to an area-efficient and timing-efficient accelerator design. The design of the accelerators invariably consists of two on-chip units: the digital filter and the matrix multiplication units. Among the features introduced are: a data-shifting means, a filter load distribution method, a reduced set of column filters, sectioned left shifters, a double-line buffer, time-multiplexed and pipelined matrix multiplication sections, and multichip amenable features. A total of 98 frames of test data from high definition videos, real and synthetic images are used in the functional tests. The single-chip field-programmable gate array implementation results show the successful realizations of accelerators capable of moment computations of (31, 31) orders, at 50 frames of 1920 × 1080 8-bit pixels per second, and (63, 63) orders, at 30 frames of 512 × 512 pixels per second. These performances have exceeded that of existing multichip and multiplatform designs.

In a rice sorting system, the separation distance of one grain from another is determined by the amount of time taken to process the image of each grain. The closer the separation distance of one grain from another will lead to higher output of grains imaged and processed. Hence, the fast computation of a reliable set of pattern features is required in a rice sorting system. Zernike moment, with its rotational invariant property makes it ideally suited to be used. Many fast computation algorithms to compute Zernike moments have been developed. In this study we propose a new application of the symmetrical property of Zernike polynomials to compute a set of Zernike moments in rice sorting system. The average overall classification time of a rice grain is reduced almost by 25.0% when the proposed technique is integrated with the q-recursive method, which currently uses the minimum time to compute a set of Zernike moments.

This paper shows how Hahn moments provide a unified understanding of the recently introduced Chebyshev and Krawtchouk moments. The two latter moments can be obtained as particular cases of Hahn moments with the appropriate parameter settings, and this fact implies that Hahn moments encompass all their properties. The aim of this paper is twofold: 1) To show how Hahn moments, as a generalization of Chebyshev and Krawtchouk moments, can be used for global and local feature extraction, and 2) to show how Hahn moments can be incorporated into the framework of normalized convolution to analyze local structures of irregularly sampled signals.

Legendre moments are continuous moments, hence, when applied to discrete-space images, numerical approximation is involved and error occurs. This paper proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically, and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Although the same set of exact Legendre moments can be obtained indirectly from the set of geometric moments, the computation time taken is much longer than the proposed method.

Pseudo-Zernike moments have better feature representation capability, and are more robust to image noise than those of the conventional Zernike moments. However, due to the computation complexity of pseudo-Zernike polynomials, pseudo-Zernike moments are yet to be extensively used as feature descriptors as compared to Zernike moments. In this paper, we propose two new algorithms, namely coefficient method and p-recursive method, to accelerate the computation of pseudo-Zernike moments. Coefficient method calculates polynomial coefficients recursively. It eliminates the need of using factorial functions. Individual order or index of pseudo-Zernike moments can be derived independently, which is useful if selected orders or indices of moments are needed as pattern features. p-recursive method uses a combination of lower order polynomials to derive higher order polynomials with the same index q. Fast computation is achieved because it eliminates the requirements of calculating polynomial coefficients, Bpqk, and power of radius, rk, in each polynomial. The performance of the proposed algorithms on moment computation and image reconstruction, as compared to those of the present methods, are experimentally verified using a set of binary and grayscale images.

In this paper, a new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials. The orthogonality of the proposed moments ensures minimal information redundancy. No numerical approximation is involved in deriving the moments, since the weighted Krawtchouk polynomials are discrete. These properties make the Krawtchouk moments well suited as pattern features in the analysis of two-dimensional images. It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features. The computational aspects of the moments using the recursive and symmetry properties are discussed. The theoretical framework is validated by an experiment on image reconstruction using Krawtchouk moments and the results are compared to that of Zernike, pseudo-Zernike, Legendre, and Tchebyscheff moments. Krawtchouk moment invariants are constructed using a linear combination of geometric moment invariants; an object recognition experiment shows Krawtchouk moment invariants perform significantly better than Hu's moment invariants in both noise-free and noisy conditions.

Two common artifacts that corrupt evoked responses are noise and background electroencephalogram (EEG). In this paper, a two-level principal component analysis (PCA) is used to reduce these artifacts from single trial evoked responses. The first level PCA is applied to reduce noise from these VEP signals while the second level PCA reduces EEG. The method is used to analyse the object recognition and decision-making capability during visual responses. The analysis is extended to study the differences in visual response between alcoholics and non-alcoholics using single trial P3 visual evoked potential (VEP) signals. The analysis shows that alcoholics respond slower and weaker to visual stimulus as compared to non-alcoholics.

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 10(3) times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.

A new technique of fusing genetic algorithms with Fuzzy ARTMAP is proposed. This method selects the appropriate autoregressive model order for EEG signals and consequently classifies these signals into their respective different mental tasks. The experimental results show that this method outperforms other statistical autoregressive model order selection methods like Akaike Information Criterion, Final Prediction Error and reflection coefficient.

Regular moment invariant face two limitations. First, images with symmetry in the x and/or y directions and symmetry at centroid give zero values for odd orders of central moments. Secondly, they are very sensitive to noise, especially the higher order moments. This paper presents a single solution to solve the symmetrical problem and reduce the noise sensitivity of these moments. The solution involves a new set of moment-based features that uses a reference point other than the image centroid. The reference centre is selected such that the new moment features are invariant to translation, scaling and rotation. The derivation of the new moments and their invariance are shown before experimenting them with some symmetrical alphabets. Next, they are shown to be less sensitive under the presence of Gaussian and random noise as compared to the usual regular moment invariants. Noise corrupted English alphabets are classified with a neural network to further verify the advantage of using the new moment features.

The classification of images using regular or geometric moment functions suffers from two major problems. First, odd orders of central moments give zero value for images with symmetry in the x and/or y directions and symmetry at centroid. Secondly, these moments are very sensitive to noise especially for higher order moments. In this paper, a single solution is proposed to solve both these problems. The solution involves the computation of the moments from a reference point other than the image centroid. The new reference centre is selected such that the invariant properties like translation, scaling and rotation are still maintained. In this paper, it is shown that the new proposed moments can solve the symmetrical problem. Next, we show that the new proposed moments are less sensitive to Gaussian and random noise as compared to two different types of regular moments derived by Hu.6 Extensive experimental study using a neural network classification scheme with these moments as inputs are conducted to verify the proposed method.