DocumentCode :
2898607
Title :
Automatic Video Object Segmentation using Wavelet Transform and Moving Edge Detection
Author :
Zhang, Xiao-yan ; Zhao, Rong-chun
Author_Institution :
Coll. of Comput., Northwestern Polytech. Univ., Xi´´an
fYear :
2006
fDate :
13-16 Aug. 2006
Firstpage :
3929
Lastpage :
3933
Abstract :
A fast and automatic video object segmentation algorithm based on wavelet transform and moving edge detection is proposed in this paper. First, the wavelet transform is applied to two consecutive frames. The change detection method with different thresholds in four wavelet sub-bands and Canny edge detection are used in wavelet domain. After the inverse wavelet transform, the robust difference edge map can be obtained. Through combination with the current frame edge map, background edge map and previous frame´s moving edge, the current frame´s moving edge can be detected and tracked. It is then used to extract video object plane (VOP) by a simple filling technique. The proposed algorithm is robust to the entire motion and local deformation of object. Experiments results and object evaluation demonstrate the effectiveness of our algorithm
Keywords :
edge detection; image segmentation; object detection; video coding; wavelet transforms; Canny edge detection; automatic video object segmentation; change detection method; edge map; inverse wavelet transform; moving edge detection; object deformation; video object plane; wavelet subbands; Change detection algorithms; Cybernetics; Image edge detection; Image segmentation; Low pass filters; Machine learning; Object detection; Object segmentation; Partitioning algorithms; Robustness; Wavelet domain; Wavelet transforms; Binary edge model; Change detection; Video object; Video object plane (VOP); Wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
Type :
conf
DOI :
10.1109/ICMLC.2006.258748
Filename :
4028757
Link To Document :
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