Wireless visual sensor networks (WVSN) have recently seen dramatic growth with technology development. These networks consist of a number of smart visual sensors (VSes) that can collect visual information, i.e., images and video captured in a network area. Optimizing the energy consumption and coverage are important contradictory challenges in WVSNs since increase in coverage leads to increase in energy consumption. Therefore, optimization of energy consumption by maintaining image quality defined by user or operator located in sink [quality of experience (QoE)] to be used in target tracking applications is addressed in this paper. The target coverage as well as the quality of the received image of the target are considered as the desired QoE. The novel two-dimensional target coverage model is also presented mathematically. This model is described as a function of the VS inherent parameters, the target position and the visual sensor position. Based on a convex optimization framework, a heuristic approach for the VS selection and the focal length adjustment is suggested to solve the optimization problem while maintaining high image quality. Simulation results are presented to verify the capability and efficiency of the proposed method in comparison with the optimal method (Exhaustive search method).

Nowadays, by emerging new technologies, demand for a wireless visual sensor networks (WVSN) has been significantly increased. By providing the vital visual data from such networks, they play a substantial role in the surveillance applications. The wireless visual sensors (VSes) are the main component of these networks, which are equipped with camera and transceiver module. Energy optimization and satisfying the quality of the captured visual data, are two main contradictable issues in this area of research, especially in the target tracking applications. Therefore, these two issues have been simultaneously investigated in this paper. The coverage of the tracked targets and the quality of the captured visual data are considered as the quality of experienced (QoE). The desired threshold of QoE is defined by user. To optimize energy consumption with regard to QoE constraints in a multi target tracking scenario, an effective VSes selection is proposed. This method has been developed based on the convex optimization framework. Besides, the focal length of the VSes is set optimally by executing the proposed algorithm. Simulation results show efficiency of the proposed method compared with the optimal method (exhaustive search).

Compressive sensing (CS) is a recently emerging technique and an extensively studied problem in signal and image processing, which suggests a new framework for the simultaneous sampling and compression of sparse or compressible signals at a rate significantly below the Nyquist rate. Maybe, designing an effective regularization term reflecting the image sparse prior information plays a critical role in CS image restoration. Recently, both local smoothness and nonlocal self-similarity have led to superior sparsity prior for CS image restoration. In this paper, first, an adaptive curvelet thresholding criterion is developed, trying to adaptively remove the perturbations appeared in recovered images during CS recovery process, imposing sparsity. Furthermore, a new sparsity measure called joint adaptive sparsity regularization (JASR) is established, which enforces both local sparsity and nonlocal 3-D sparsity in transform domain, simultaneously. Then, a novel technique for high-fidelity CS image recovery via JASR is proposed-CS-JASR. To efficiently solve the proposed corresponding optimization problem, we employ the split Bregman iterations. Extensive experimental results are reported to attest the adequacy and effectiveness of the proposed method comparing with the current state-of-the-art methods in CS image restoration.

In this paper, we study transmission of a memoryless Laplacian source over three types of channels: additive white Laplacian noise (AWLN), additive white Gaussian noise (AWGN), and slow flat-fading Rayleigh channels under both bandwidth compression and bandwidth expansion. For this purpose, we analyze two well-known hybrid digital-analog (HDA) joint source-channel coding schemes for bandwidth compression and one for bandwidth expansion. Then we obtain achievable (absolute-error) distortion regions of the HDA schemes for the matched signal-to-noise ratio (SNR) case as well as the mismatched SNR scenario. Using numerical examples, it is shown that these schemes can achieve a distortion very close to the provided lower bound (for the AWLN channel) and to the optimum performance theoretically attainable bound (for AWGN and Rayleigh fading channels) on mean-absolute error distortion under matched SNR conditions. In addition, a non-linear analog coding scheme is analyzed, and its performance is compared to the HDA schemes for bandwidth compression under both matched and mismatched SNR scenarios. The results show that the HDA schemes outperform the non-linear analog coding over the whole CSNR region.

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

Contrast enhancement by the unsharp masking (UM) approaches are computationally and conceptually the simplest methods. In this paper, the different methods of UM for image enhancement are compared. These algorithms have been tested and compared for the same conditions. The results show that majority of these algorithms are very sensitive to the enhancement factor and are good for sharpening. This factor must be estimated recursively by considering the statistics of neighboring pixel values.

Face expression recognition is useful for designing new interactive devices offering the possibility of new ways for human to interact with computer systems. In this paper, we develop a facial expression recognition system, based on the facial features extracted from facial characteristic points in frontal image sequences. Selected facial feature points were automatically tracked using a cross-correlation based optical flow, and extracted feature vectors were used to classify expressions, using RBF neural networks and a fuzzy inference system (FIS). Then, recognition results from two classifiers were compared with each other. Success rates were about 91.6% using RBF and 89.1% using FIS classifiers