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Conference papers

Cooperative disparity and object boundary estimation

Ramya Narasimha 1 Elise Arnaud 1 Florence Forbes 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : In this paper we carry out cooperatively both disparity and object boundary estimation by setting the two tasks in a unified Markovian framework. We introduce a new joint probabilistic model that allows to estimate disparities through a Markov random field model. Boundary estimation then cooperates with disparity estimation to gradually and jointly improve accuracy. The feedback from boundary estimation to disparity estimation is made through the use of an auxiliary field referred to as a displacement field. This field suggests the corrections that need to be applied at disparity discontinuities in order that they align with object boundaries. The joint model reduces to a Markov random field model when considering disparities while it reduces to a Markov chain when focusing on the displacement field. The performance of our approach is illustrated on real stereo images sets, demonstrating the power of this cooperative framework.
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Submitted on : Monday, July 28, 2008 - 10:50:41 AM
Last modification on : Tuesday, October 19, 2021 - 11:13:07 PM
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Ramya Narasimha, Elise Arnaud, Florence Forbes, Radu Horaud. Cooperative disparity and object boundary estimation. ICIP 2008 - 15th IEEE International Conference on Image Processing, Oct 2008, San Diego, United States. pp.1784-1787, ⟨10.1109/ICIP.2008.4712122⟩. ⟨inria-00306582⟩



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