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Research Article | Open Access

Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming

Mohamed Hallek1( ), Fethi Smach2, Mohamed Atri1
Faculty of Sciences of Monastir, 5000 Monastir, Tunisia.
Technologies et services de l’information, Paris, France.
Show Author Information

Abstract

Computation of stereoscopic depth and disparity map extraction are dynamic research topics. A large variety of algorithms has been developed, among which we cite feature matching, moment extraction, and image representation using descriptors to determine a disparity map. This paper proposes a new method for stereo matching based on Fourier descriptors. The robustness of these descriptors under photometric and geometric transformations provides a better representation of a template or a local region in the image. In our work, we specifically use generalized Fourier descriptors to compute a robust cost function. Then, a box filter is applied for cost aggregation to enforce a smoothness constraint between neighboringpixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptorsusing Euclidean distance. This local cost functionis used to optimize correspondences. Our stereo matching algorithm is evaluated using the Middlebury stereo benchmark; our approach has been implemented on parallel high-performance graphics hardware using CUDA to accelerate our algorithm, giving a real-time implementation.

Keywords

dynamic programminggeneralized Fourier descriptorsstereo matchingCUDA

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Computational Visual Media
Volume 5 Issue 1,
March 2019
Pages 59-71
DOI: 10.1007/s41095-019-0133-4
Cite this article:
Hallek M, Smach F, Atri M. Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming. Computational Visual Media, 2019, 5(1): 59-71. https://doi.org/10.1007/s41095-019-0133-4

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Revised: 05 November 2018
Accepted: 27 January 2019
Published: 08 April 2019
© The Author(s) 2019

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