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Feature-Level Fusion for Object Segmentation Using Mutual Information

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Book cover Augmented Vision Perception in Infrared

Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

A new feature-level image fusion technique for object segmentation is presented. The proposed technique approaches fusion as a feature selection problem, utilizing a selection criterion based on mutual information. Starting with object regions roughly detected from one sensor, the proposed technique aims to extract relevant information from another sensor to best complete the object segmentation. First, a contour-based feature representation is presented that implicitly captures object shape. The notion of relevance across sensor modalities is then defined using mutual information computed based on the affinity between contour features. Finally, a heuristic selection scheme is proposed to identify the set of contour features having the highest mutual information with the input object regions. The approach works directly from the input image pair without relying on a training phase. The proposed algorithm is evaluated using a typical surveillance setting. Quantitative results and comparative analysis with other potential fusion methods are presented.

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Ohio State University, Columbus, OH, 43210, USA

    Vinay Sharma & James W. Davis

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  1. Vinay Sharma
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  2. James W. Davis
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Editors and Affiliations

  1. Delphi Electronics & Safety Kokomo, Indiana, USA

    Riad I. Hammoud

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© 2009 Springer-Verlag Berlin Heidelberg

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Sharma, V., Davis, J.W. (2009). Feature-Level Fusion for Object Segmentation Using Mutual Information. In: Hammoud, R.I. (eds) Augmented Vision Perception in Infrared. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-277-7_13

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  • DOI: https://doi.org/10.1007/978-1-84800-277-7_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-276-0

  • Online ISBN: 978-1-84800-277-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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