Disparity of Stereo Images by Self-Adaptive Algorithm

This paper introduces a new searching method named “Self Adaptive Algorithm (SAA)” for computing stereo correspondence or disparity of stereo image. The key idea of this method relies on the previous search result which increases searching speed by reducing the search zone and by avoiding false matching. According to the proposed method, stereo matching search range can be selected dynamically until finding the best match. The searching range -dmax to +dmax is divided into two searching regions. First one is -dmax to 0 and second one is 0 to +dmax .To determine the correspondence of a pixel of the reference image (left image), the window costs of the right image are computed either for -dmax to 0 region or for 0 to +dmax region depending only on the matching pixel position. The region where the window costs will be computed- will be automatically selected by the proposed algorithm based on previous matching record. Thus the searching range is reduced to 50% within every iteration. The algorithm is able to infer the upcoming candidate’s pixel position depending on the intensity value of reference pixel. So the proposed approach improves window costs calculation by avoiding false matching in the right image and reduces the search range as well. The proposed method has been compared with the state-of-the-art methods which were evaluated on Middlebury standard stereo data set and our SAA outperforms the latest methods both in terms of speed and gain enhancement with no degradation of accuracy.

@article{Mondal2020,
  title     = {Disparity of Stereo Images by Self-Adaptive Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Md. Abdul Mannan Mondal and Mohammad Haider Ali},
  doi       = {10.14569/IJACSA.2020.0110558},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110558}
}