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Fully automatic algorithm for estimating the 3D human pose from multiple uncalibrated cameras is presented. Unlike the state-of-the-art methods which use the estimated pose of previous frames to restrict the candidates of current frame, the proposed method uses the viewpoint of previous frame in order to obtain an accurate pose. This paper also introduces a method to incorporate pose estimation results of several cameras without using the calibration information. The algorithm employs a rich descriptor for matching purposes. The performance of the proposed method is evaluated on a soccer database which is captured by multiple cameras. The dataset of silhouettes, in which the related 3D skeleton poses are known, is also constructed. Experimental results show that the proposed algorithm has a high accuracy rate in estimation of 3D pose of soccer players.

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Real images contain different types of noises and a very difficult process is to remove mixed noise in any type of them. Additive White Gaussian Noise (AWGN) coupled with Impulse Noise (IN) is a typical method. Many mixed noise removal methods are based on a detection method that generates artificial products in case of high noise levels. In this article, we suggest an active weighted approach for mixed noise reduction, defined as Weighted Encoding Sparse Noise Reduction (WESNR), encoded in sparse non-local regulation. The algorithm utilizes a non-local self-similarity feature of image in the sparse coding framework and a pre-learned Principal Component Analysis (PCA) dictionary. Experimental results show that both the quantitative and the visual quality, the proposed WESNR method achieves better results of the other technique in terms of PSNR.

In numerical computation of neuromodulator such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), the quality of the tetrahedral mesh is a significant criterion to achieve accurate results in simulating the electric field distribution. Although there are various algorithms and software products have been proposed to achieve this goal, but most of them have challenges in ease of use or in the quality of the final mesh. Even with the production of tetrahedral mesh, there is still a deficiency in the quality of the tetrahedral mesh that leads to reduced accuracy in the final result. In this paper, a new MATLAB toolbox (SimUTab) a is presented that uses several useful algorithms in this field and also employs new methods to generate high-quality meshes for head models. The proposed method reduces the difficulties and makes it easy to use. The user interface implemented in MATLAB makes easily designing electrodes even with unusual shapes and hence provides the basis for examining the effect of electrode shapes on the electric field distribution of the target area. The proposed method is tested and compared with other active pipelines. The results confirm the superiority of the proposed method and show an improvement in the quality of the final mesh. a The proposed toolbox (SimUTab) with several examples can be downloaded for free from GitHub: https://github.com/amjad1986/tDSC-TOOLBOX INDEX TERMS transcranial direct current stimulation, structural magnetic resonance segmentation, finite elements method (FEM) analysis, forward electric field calculation. , 1