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2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2
Background Subtraction based on Cooccurrence of Image Variations
Madison, Wisconsin
June 18-June 20
ISBN: 0-7695-1900-8
| ASCII Text | x | ||
| Makito Seki, Toshikazu Wada, Hideto Fujiwara, Kazuhiko Sumi, "Background Subtraction based on Cooccurrence of Image Variations," 2012 IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 65, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003. | |||
| BibTex | x | ||
| @article{ 10.1109/CVPR.2003.1211453, author = {Makito Seki and Toshikazu Wada and Hideto Fujiwara and Kazuhiko Sumi}, title = {Background Subtraction based on Cooccurrence of Image Variations}, journal ={2012 IEEE Conference on Computer Vision and Pattern Recognition}, volume = {2}, year = {2003}, issn = {1063-6919}, pages = {65}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211453}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE Conference on Computer Vision and Pattern Recognition TI - Background Subtraction based on Cooccurrence of Image Variations SN - 1063-6919 SP EP A1 - Makito Seki, A1 - Toshikazu Wada, A1 - Hideto Fujiwara, A1 - Kazuhiko Sumi, PY - 2003 KW - null VL - 2 JA - 2012 IEEE Conference on Computer Vision and Pattern Recognition ER - | |||
This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.
Citation:
Makito Seki, Toshikazu Wada, Hideto Fujiwara, Kazuhiko Sumi, "Background Subtraction based on Cooccurrence of Image Variations," cvpr, vol. 2, pp.65, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003
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