DocumentCode :
419431
Title :
Bayesian object-level change detection in grayscale imagery
Author :
Perera, A. G Amitha ; Hoogs, Anthony
Author_Institution :
GE Global Res., Niskayuna, NY, USA
Volume :
1
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
71
Abstract :
We present a change detection algorithm formulated in a Bayesian framework that uses the output of an object detector to reason about change at a higher level than comparing pixels. The object detector mitigates pixel-level noise, and presents objects to the change detection framework. This in turn ties the objects across images and determines change. The Bayesian framework allows us to easily add domain knowledge into the change detection process to improve detection. We show that our approach can successfully detect changes across grayscale images with significantly greater variance in imaging conditions (such as viewpoint, resolution, and illumination) than those handled by traditional methods.
Keywords :
Bayes methods; image processing; object detection; Bayesian object detection; change detection algorithm; grayscale imagery; image pixel level noise; object detector; Bayesian methods; Calibration; Detectors; Gray-scale; Layout; Lighting; Object detection; Pixel; Vehicle detection; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1334007
Filename :
1334007
Link To Document :
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