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Biologically-inspired robust motion segmentation using mutual information

Ellis, A.-L. and Ferryman, J. (2014) Biologically-inspired robust motion segmentation using mutual information. Computer Vision and Image Understanding, 122. 47 - 64. ISSN 1077-3142

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To link to this item DOI: 10.1016/j.cviu.2014.01.009


This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36796
Uncontrolled Keywords:Biologically-inspired vision; Background modelling; Segmentation; Surveillance; Performance evaluation


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