Abstract-one of the best way to clustering in color images is to transform R, G, B color space into the target color space by linear transformations that are captured by 3×3 matrices. The Main target of this paper is introducing new color transform from viewpoint of convex constraint programming. Lip detection is used as benchmark problem for the proposed algorithm. In the New color space, the Lip and non-Lip classes are separated as well. This problem is converted to a convex constraint programming which Genetic Algorithm is used for solving this problem. Founded converting matrix is tested in Lip detection in simple to complex scene. Obtained results over many databases are compared with existing methods which show superiority of the proposed method.

In this paper considering a n e w human gait recognition system based on Radon transform which gives a high precision recognition rate. Innovation of this paper allocate to feature extraction and usage of them during process by combined neural networks. feature extraction is based on the Radon transform of binary silhouettes .in this paper For each gait sequence, the transformed silhouettes are used after background estimation and human detection in the scene to make each related template's. then set of all templates is used to subspace projection by following PCA method and earning final decimated feature vector for each persons in database. consequently earned feature vector for each person's is applied to multilayer perceptron neural network and set of all neural networks feed to final neural network for final decision. Experimental results is performed over a suitable data base include 10 samples for ten person which each sample have 130 frames approximately. 97% recognition rate of the proposed system is obtained over 10 samples test patterns.

This Each three-component collection such as {Red, Green, Blue} (RGB), and {Luminance Y, Chrominance Cr, Chrominance Cb} (YCbCr) is termed as a color space. Many color spaces are related to each other by linear transformations that are captured by 3×3 matrices. Hence a given color, and thereby any color image, can be represented in terms of another color space by transforming its 3-d vector representation using the 3 × 3 matrix. The Main target of this paper is introduce new color transform from viewpoint of convex constraint programming. Skin detection is used as benchmark problem for the proposed algorithm. In the New color space, the skin and non-skin classes are separated as well. This problem is converted to a convex constraint programming which Lagrange multipliers method is used for solving this problem. Founded converting matrix is tested in skin detection in simple to complex scene. Obtained results over many databases are compared with existing methods which show superiority of the proposed method. Skin and non-skin clusters in the new space color have clustering criteria better than RGB and YCbCr color space.

This study aimed to investigate the relationship between employees' perception of organizational justice and social capital in The National Tax Administration in the south of Tehran province. This is an applied study in terms of purpose, descriptive in terms of nature, and survey in terms of method. The statistical population included all employees of the National Tax Administration in the south of Tehran province. According to Morgan and Krejcie's table (1990), 214 people were selected as the sample size in simple random sampling method. Talebzadeh Organizational Justice Questionnaire (2011) and Kennedy Social Capital Questionnaire were used to collecting data. The research hypotheses were examined to analyze the data using the Pearson correlation coefficient test. The study results showed a positive and significant relationship between employees' perception of distributive justice, procedural justice, interactional justice, information justice, and the total score of organizational justice with the social capital.

Abstract-PC-SVM is a new developed support vector machine classifier with probabilistic constrains which presence of samples probability in each class is determined based on a distribution function. The presence of noise causes incorrect calculation of support vectors thereupon margin can not be maximized. In the Pc-SVM, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin. The main target of this paper is introducing a robust visual object recognition based on PC-SVM. Human detection is used as benchmark problem for the proposed algorithm. Experimental results show superiority of the probabilistic constraints support vector machine (PC-SVM) relative to standard SVM in human detection.