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Towards a Bayesian Approach to Robust Finding Correspondences in Multiple View Geometry Environments

  • Cristian Canton-Ferrer
  • Josep R. Casas
  • Montse Pardàs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)

Abstract

This paper presents a new Bayesian approach to the problem of finding correspondences of moving objects in a multiple calibrated camera environment. Moving objects are detected and segmented in multiple cameras using a background learning technique. A Point Based Feature (PBF) of each foreground region is extracted, in our case, the top. This features will be the support to establish the correspondences. A reliable, efficient and fast computable distance, the symmetric epipolar distance, is proposed to measure the closeness of sets of points belonging to different views. Finally, matching the features from different cameras originating from the same object is achieved by selecting the most likely PBF in each view under a Bayesian framework. Results are provided showing the effectiveness of the proposed algorithm even in case of severe occlusions or with incorrectly segmented foreground regions.

Keywords

Camera View Epipolar Line Epipolar Geometry Foreground Region Correspondence Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Cristian Canton-Ferrer
    • 1
  • Josep R. Casas
    • 1
  • Montse Pardàs
    • 1
  1. 1.Image Processing GroupTechnical University of CataloniaBarcelonaSpain

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