Towards a Bayesian approach to Robust finding correspondences in multiple view geometry environments

20Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

This article is free to access.

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. © Springer-Verlag Berlin Heidelberg 2005.

Cite

CITATION STYLE

APA

Canton-Ferrer, C., Casas, J. R., & Pardàs, M. (2005). Towards a Bayesian approach to Robust finding correspondences in multiple view geometry environments. In Lecture Notes in Computer Science (Vol. 3515, pp. 281–289). Springer Verlag. https://doi.org/10.1007/11428848_35

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free