Stereo disparity refinement using genetic optimization

Victor H. Diaz-Ramirez; Leopoldo N. Gaxiola; Juan Pablo Apun; Rigoberto Juarez-Salazar

doi:10.1117/12.3028780

30 September 2024 Stereo disparity refinement using genetic optimization

Victor H. Diaz-Ramirez, Leopoldo N. Gaxiola, Juan Pablo Apun, Rigoberto Juarez-Salazar

Author Affiliations +

Proceedings Volume 13136, Optics and Photonics for Information Processing XVIII; 131360D (2024) https://doi.org/10.1117/12.3028780
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Disparity refinement is a post-processing step in stereo vision that retrieves unknown disparity values caused by pixel occlusions or estimation errors. This step is crucial for improving depth estimation accuracy and reducing artifacts. In this work, we propose an iterative method based on genetic optimization to perform disparity refinement for stereo vision. The estimation of unknown disparity values is formulated as an optimization problem, where a fitness function is optimized by minimizing a trade-off between disparity variations and point correspondence errors. The proposed method achieves accurate refined disparity maps for stereo depth estimation. Computer simulation results are presented and discussed in terms of objective performance measures. Additionally, the results are compared with those obtained using a well-known existing method.

Conference Presentation

Citation Download Citation

Victor H. Diaz-Ramirez, Leopoldo N. Gaxiola, Juan Pablo Apun, and Rigoberto Juarez-Salazar "Stereo disparity refinement using genetic optimization", Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 131360D (30 September 2024); https://doi.org/10.1117/12.3028780

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