Improved Foreground Detection via Block-Based Classifier Cascade With Probabilistic Decision Integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 23; H. 1; S. 83 - 93
Hauptverfasser:	Reddy, V., Sanderson, C., Lovell, B. C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.01.2013 Institute of Electrical and Electronics Engineers
Schlagworte:	Accuracy Adaptation models Applied sciences Artificial intelligence Background modeling background subtraction cascade Computer science; control theory; systems Detection, estimation, filtering, equalization, prediction Exact sciences and technology foreground detection Histograms Image color analysis Image processing Image segmentation Information, signal and communications theory Lighting patch analysis Pattern recognition Pattern recognition. Digital image processing. Computational geometry Probabilistic logic segmentation Signal and communications theory Signal processing Signal, noise Telecommunications and information theory Performance evaluation Low-dimensional system Automatic classification Segmentation Image processing Noise reduction patch analysis Information extraction Shape detection Modeling Texture Accuracy Image sequence Illumination Computer vision foreground detection Target tracking Probabilistic approach Background cascade Algorithm Signal classification Background modeling Information processing Computer applications Signal processing Foreground background subtraction
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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