Illumination-Invariant Change Detection Using a Statistical Colinearity Criterion
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Detected motion classification with a double-background and a neighborhood-based difference
Pattern Recognition Letters
Real-Time Motion Estimation and Visualization on Graphics Cards
VIS '04 Proceedings of the conference on Visualization '04
Change detection using a statistical model in an optimally selected color space
Computer Vision and Image Understanding
Objects based change detection in a pair of gray-level images
Pattern Recognition
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Image Processing
Novel region-based modeling for human detection within highly dynamic aquatic environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Comparison between different illumination independent change detection techniques
Proceedings of the 2011 International Conference on Communication, Computing & Security
Retrieval of changes in moving objects in multiple and color images
Proceedings of the 2011 International Conference on Communication, Computing & Security
Content aware video manipulation
Computer Vision and Image Understanding
Performance analysis of homomorphic systems for image change detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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In this paper, an illumination-independent statistical change detection method is proposed. The proposed method consists of two parts. First, based on our defined circular shift moments, structural changes can be distinguished from those due to time-varying illumination in the noise-free case. Moreover, the amount of computation is less than that of the shading model method. Second, in the light of the characteristics of the defined moments, a statistical decision rule is also proposed to cope with the effects of noise. The change detection problem can be treated as one of hypothesis testing. Critical values can be chosen according to the desired level of significance. Experimental results indicate that the proposed method detects changes accurately in the time-varying illumination case