Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

Stereo vision is one of the most active research areas in modern computer vision. The objective is to recover 3-D depth information from a pair of 2-D images that capture the same scene. This paper addresses the problem of dense stereo correspondence, where the goal is to determine which image pixels in both images are projections of the same 3-D point from the observed scene. The proposal in this work is to build a non-linear operator that combines three well known methods to derive a correspondence measure that allows us to retrieve a better approximation of the ground truth disparity of stereo image pair. To achieve this, the problem is posed as a search and optimization task and solved with genetic programming (GP), an evolutionary paradigm for automatic program induction. Experimental results on well known benchmark problems show that the combined correspondence measure produced by GP outperforms each standard method, based on the mean error and the percentage of bad pixels. In conclusion, this paper shows that GP can be used to build composite correspondence algorithms that exhibit a strong performance on standard tests.

Keywords

Stereo Vision Disparity Map Genetic Programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Enrique Naredo
    • 1
  • Enrique Dunn
    • 2
  • Leonardo Trujillo
    • 1
  1. 1.Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y ElectrónicaInstituto Tecnológico de TijuanaTijuanaMéxico
  2. 2.University of North Carolina, at Chapel HillChapel HillUSA

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