TAILIEUCHUNG - Báo cáo sinh học: " Research Article Local Histogram of Figure/Ground Segmentations for Dynamic Background Subtraction"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Local Histogram of Figure/Ground Segmentations for Dynamic Background Subtraction | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 782101 14 pages doi 2010 782101 Research Article Local Histogram of Figure Ground Segmentations for Dynamic Background Subtraction Bineng Zhong 1 Hongxun Yao 1 Shaohui Liu 1 and Xiaotong Yuan2 1 Department of Computer Science and Engineering Harbin Institute of Technology West Da-Zhi Street Harbin Heilongjiang 150001 China 2 National Laboratory of Pattern Recognition Institute of Automation CAS Beijing 100080 China Correspondence should be addressed to Bineng Zhong bnzhong@ Received 23 October 2009 Revised 22 April 2010 Accepted 9 June 2010 Academic Editor Irene Y. H. Gu Copyright 2010 Bineng Zhong et al. 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. We propose a novel feature local histogram of figure ground segmentations for robust and efficient background subtraction BGS in dynamic scenes . waving trees ripples in water illumination changes camera jitters etc. . We represent each pixel as a local histogram of figure ground segmentations which aims at combining several candidate solutions that are produced by simple BGS algorithms to get a more reliable and robust feature for BGS. The background model of each pixel is constructed as a group of weighted adaptive local histograms of figure ground segmentations which describe the structure properties of the surrounding region. This is a natural fusion because multiple complementary BGS algorithms can be used to build background models for scenes. Moreover the correlation of image variations at neighboring pixels is explicitly utilized to achieve robust detection performance since neighboring pixels tend to be similarly affected by environmental effects . dynamic scenes . Experimental results demonstrate the robustness and .

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