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Parallel framework for dense disparity map estimation using Hamming distance

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Abstract

A novel framework for sparse and dense disparity estimation was designed, and the proposed framework has been implemented in CPU and GPU for a parallel processing capability. The Census transform is applied in the first stage, and then, the Hamming distance is later used as similarity measure in the stereo matching stage followed by a matching consistency check. Next, a disparity refinement is performed on the sparse disparity map via weighted median filtering and color K-means segmentation, in addition to clustered median filtering to obtain the dense disparity map. The results are compared with state-of-the-art frameworks, demonstrating this process to be competitive and robust. The quality criteria used are structural similarity index measure and percentage of bad pixels (B) for objective results and subjective perception via human visual system demonstrating better performance in maintaining fine features in disparity maps. The comparisons include processing times and running environments, to place each process into context.

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Acknowledgements

The authors would like to thank Instituto Politcnico Nacional, Consejo Nacional de Ciencia y Tecnologia (Project 220347) and Secretaria de Ciencia, Tecnologia e Innovacion del D.F. (Mexico) for their support in accomplishing this work.

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Authors and Affiliations

  1. ESIME Culhuacan, Instituto Politecnico Nacional, Av. Santa Anna 1000, Mexico City, Mexico

    Victor Gonzalez-Huitron & Volodymyr Ponomaryov

  2. Universidad Autonoma de la Ciudad de Mexico, Prolongacion San Isidro 151, Mexico City, Mexico

    Eduardo Ramos-Diaz

  3. Instituto Mexicano del Petroleo, Lazaro Cardenas 152, Mexico City, Mexico

    Sergiy Sadovnychiy

Authors
  1. Victor Gonzalez-Huitron
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  2. Volodymyr Ponomaryov
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  3. Eduardo Ramos-Diaz
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  4. Sergiy Sadovnychiy
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Correspondence to Victor Gonzalez-Huitron.

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Gonzalez-Huitron, V., Ponomaryov, V., Ramos-Diaz, E. et al. Parallel framework for dense disparity map estimation using Hamming distance. SIViP 12, 231–238 (2018). https://doi.org/10.1007/s11760-017-1150-3

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  • DOI: https://doi.org/10.1007/s11760-017-1150-3

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