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DOI: 10.14569/IJACSA.2017.081144
PDF

A Comparative Study of Stereovision Algorithms

Author 1: Elena Bebeselea-Sterp
Author 2: Raluca Brad
Author 3: Remus Brad

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 11, 2017.

  • Abstract and Keywords
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Abstract: Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel implementations.

Keywords: Stereo vision; disparity; correspondence; comparative study; middlebury benchmark

Elena Bebeselea-Sterp, Raluca Brad and Remus Brad, “A Comparative Study of Stereovision Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081144

@article{Bebeselea-Sterp2017,
title = {A Comparative Study of Stereovision Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081144},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081144},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {11},
author = {Elena Bebeselea-Sterp and Raluca Brad and Remus Brad}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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