Fusing Complementary Operators to Enhance Foreground/Background Segmentation
Al-Mazeed, Ahmad H., Nixon, Mark S. and Gunn, Steve R. (2003) Fusing Complementary Operators to Enhance Foreground/Background Segmentation. In, British Machine Vision Conference 2003, Norwich, BMVA Press, 501-510.
Foreground/background segmentation is an active research area for moving object analysis. We combine two probabilistic approaches one of which estimates foreground/background probabilistic density and the other uses prior knowledge to decompose the colour space. The observed performance advantages are associated with the fusion of operators with completely different basis. Tests on outdoor and indoor sequences confirm the efficacy of this approach. The new algorithms can successfully identify and remove shadows and highlights with improved moving-object segmentation. A particular advantage of our evaluation is that it is the first approach that compares foreground/ background labelling with results obtained from labelling by broadcast techniques.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Event Dates: 2003|
|Keywords:||Motion Segmentation, Mixture Models, Foregraound/ Background Labelling, Gait|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Electronic & Software Systems
|Date Deposited:||20 Nov 2003|
|Last Modified:||27 Mar 2014 20:00|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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