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International Conference on Scale-Space Theories in Computer Vision

Scale-Space 2001: Scale-Space and Morphology in Computer Vision pp 85–97Cite as

Bayesian Object Detection through Level Curves Selection

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Part of the book series: Lecture Notes in Computer Science 2106 ((LNCS,volume 2106))

Abstract

Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image detection, the a priori distribution should capture the knowledge about objects. Taking inspiration from [1], we design a prior density that penalizes the area of homogeneous parts in images. The detection problem is further formulated as the estimation of the set of curves that maximizes the posterior distribution. In this paper, we explore a posterior distribution model for which its maximal mode is given by a subset of level curves, that is the boundaries of image level sets. For the completeness of the paper, we present a stepwise greedy algorithm for computing partitions with connected components.

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Author information

Authors and Affiliations

  1. INRA - Biométrie, Domaine de Vilvert, 78352, Jouy-en-Josas, France

    Charles Kervrann

Authors
  1. Charles Kervrann
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Editor information

Editors and Affiliations

  1. Dept. of Mathematics & Computer Science, University of Richmond, Richmond, VA, 23173, USA

    Michael Kerckhove

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© 2001 Springer-Verlag Berlin Heidelberg

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Kervrann, C. (2001). Bayesian Object Detection through Level Curves Selection. In: Kerckhove, M. (eds) Scale-Space and Morphology in Computer Vision. Scale-Space 2001. Lecture Notes in Computer Science 2106, vol 2106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47778-0_8

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  • DOI: https://doi.org/10.1007/3-540-47778-0_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42317-1

  • Online ISBN: 978-3-540-47778-5

  • eBook Packages: Springer Book Archive

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