Disparity and normal estimation through alternating maximization

Ramya Narasimha 1 Elise Arnaud 2 Florence Forbes 3 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 STEEP - Sustainability transition, environment, economy and local policy
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, we propose an algorithm that recovers binocular disparities in accordance with the surface properties of the scene under consideration. To do so, we estimate the disparity as well as the normals in the disparity space, by setting the two tasks in a unified framework. A novel joint probabilistic model is defined through two random fields to favor both intra field (within neighboring disparities and neighboring normals) and inter field (between disparities and normals) consistency. Geometric contextual information is introduced in the models for both normals and disparities, which is optimized using an appropriate alternating maximization procedure. We illustrate the performance of our approach on synthetic and real data.
Document type :
Conference papers
ICIP 2010 - 17 IEEE International Conference on Image Processing, Sep 2010, Honk Kong, China. IEEE, pp.2969-2972, 2010, <10.1109/ICIP.2010.5653453>
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Submitted on : Wednesday, September 15, 2010 - 5:19:36 PM
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Ramya Narasimha, Elise Arnaud, Florence Forbes, Radu Horaud. Disparity and normal estimation through alternating maximization. ICIP 2010 - 17 IEEE International Conference on Image Processing, Sep 2010, Honk Kong, China. IEEE, pp.2969-2972, 2010, <10.1109/ICIP.2010.5653453>. <inria-00517864>

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