tailieunhanh - Báo cáo hóa học: " Research Article Robust Object Categorization and Segmentation Motivated by Visual Contexts in the Human Visual System"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Robust Object Categorization and Segmentation Motivated by Visual Contexts in the Human Visual System | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 101428 22 pages doi 2011 101428 Research Article Robust Object Categorization and Segmentation Motivated by Visual Contexts in the Human Visual System Sungho Kim Yeungnam University 214-1 Dae-Dong Gyeongsan-Si Gyeongsangbuk-Do 712-749 Republic of Korea Correspondence should be addressed to Sungho Kim sunghokim@ Received 7 April 2010 Accepted 9 November 2010 Academic Editor Steven McLaughlin Copyright 2011 Sungho Kim. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as novel object learning and topological place recognition. The main difficulties of visual categorization are two folds large internal and external variations caused by surface markings and background clutters respectively. In this paper we present a new object categorization method robust to surface markings and background clutters. Biologically motivated codebook selection method alleviates the surface marking problem. Introduction of visual context to the codebook approach can handle the background clutter issue. The visual contexts utilized are part-part context part-whole context and object-background context. The additional contribution is the proposition of a statistical optimization method termed boosted MCMC to incorporate the visual context in the codebook approach. In this framework three kinds of contexts are incorporated. The object category label and figure-ground information are estimated to best describe input images. We experimentally validate the effectiveness and feasibility of object categorization in cluttered environments. 1. Introduction Intelligent mobile robots should have visual perception .