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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2010 Vol.2(5): 692-694 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2010.V2.226

A Robust Method Applied to Human Detection

Seyyed Meysam Hosseini, Hasan Farsi
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.

Index Terms—pc-svm, human detection, histograms of oriented gradients.

S. M. Hosseini is with the Islamic Azad University Of Firoozkooh, electrical engineering group, Iran; e-mail: M.hosseini@ Birjand.ac.ir).
H. Farsi was with University Of Birjand, Iran. He is now with the Department of Electrical Engineering, Birjand, Iran (e-mail: HFarsi@Birjand.ac.ir).


Cite: Seyyed Meysam Hosseini, Hasan  Farsi, "A  Robust  Method  Applied  to  Human  Detection,"  International
Journal of Computer Theory and Engineering
vol. 2, no. 5, pp. 692-694, 2010.
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