DocumentCode :
2036161
Title :
Topological-Stabilization Based Threshold Quantization for Robust Change Detection
Author :
Su, Chang ; Amer, Aishy
Author_Institution :
Concordia Univ., Quebec
Volume :
3
fYear :
2007
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
A threshold quantization algorithm for robust change detection is proposed in this paper. According to the threshold distribution of difference frames, a 4-level Lloyd-Max quantizer is designed, and then, based on the topological stabilization of video frames, the Lloyd-Max quantizer is refined by a linear adjusting function to form the proposed threshold quantizer. Objective and subjective experiments show that the proposed quantizer greatly improves the robustness of the thresholding methods for change detection thus significantly improves the quality of change masks without increasing computation loads.
Keywords :
image segmentation; quantisation (signal); stability; statistical distributions; topology; video coding; Lloyd-Max quantizer; image segmentation; robust change detection; threshold distribution; threshold quantization; topological stabilization; video frames; Additive white noise; Change detection algorithms; Distributed computing; Histograms; Image processing; Image segmentation; Layout; Quantization; Robustness; Video coding; Image processing; image segmentation; quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2007.4379318
Filename :
4379318
Link To Document :
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