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An Efficient Adaptive Window Based Disparity Map Computation Algorithm by Dense Two Frame Stereo Correspondence

  • Narendra Kumar Shukla
  • Vivek Rathi
  • Vijaykumar Chakka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

This paper presents an efficient algorithm for disparity map computation with an adaptive window by establishing two frame stereo correspondence. Adaptive window based approach has a clear advantage of producing dense depth maps from stereo images. In recent years there has not been much research on adaptive window based approach due its high complexity and large computation time. Adaptive window based method selects an appropriate rectangular window by evaluating the local variation of the intensity and the disparity. Ideally the window need not be rectangular but to reduce algorithmic complexity and hence computation time, rectangular window is taken. There is a need for correction of errors introduced due to the rectangular window which is not dealt by the existing algorithm. To reduce this error, a method has been proposed which not only improves the disparity maps but also has a lesser computational complexity. To demonstrate the effectiveness of the algorithm the experimental results from synthetic and real image pairs (provided by middlebury research group) including ones with ground-truth values for quantitative comparison with the other methods are presented. The proposed algorithm outperforms most of the existing algorithms evaluated in the taxonomy of dense two frame stereo algorithms. The implementation has been done in C++. The algorithm has been tested with the standard stereo pairs which are used as benchmark for comparison of algorithms in the taxonomy implementation.

Keywords

Window Size Rectangular Window Bold Face Disparity Estimate Ground Truth Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluaion of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)MATHCrossRefGoogle Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Narendra Kumar Shukla
    • 1
  • Vivek Rathi
    • 1
  • Vijaykumar Chakka
    • 1
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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