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    US Patent Patent# 9.317.923 was awarded on 04/19/2016.

 It is evident that the accuracy of stereo vision algorithms has continued to increase based on commonly used quantitative evaluations of the resulting disparity maps. This work focuses on the development of promising stereo vision algorithms that efficiently trade off accuracy for large reductions in required computational resources. An intensity profile shape matching algorithm is introduced as an example of an algorithm that makes such tradeoffs. The proposed algorithm is compared to both a basic sum-of-absolute-differences (SAD) block matching algorithm, as well as a stereo vision algorithm that is highly ranked for its accuracy based on the Middlebury evaluation criteria. This comparison shows that the proposed algorithm’s accuracy on the commonly used Tsukuba stereo image pair is lower than many published stereo vision algorithms, but that for unrectified stereo image pairs that have even the slightest differences in brightness and/or contrast, it is far more robust than algorithms that rely on SAD block matching. An example application that requires 3D information is implemented to show that the accuracy of the proposed algorithm is sufficient for this use. Timing results show that this is a very fast dense-disparity stereo vision algorithm when compared to other algorithms capable of running on a standard microprocessor.
Project Sponsors:   David and Deborah Huber Scholarship

Graduate Students:

  Beau Tippetts and Pengcheng Zhan

  1. B.J. Tippetts, D.J. Lee, K.D. Lillywhite, and J.K Archibald, “Review of Stereo Vision Algorithms and their Suitability for Resource Limited Systems,” Journal of Real-Time Image Processing, vol. 11/1, p. 5-25, January 2016.
  2. B.J. Tippetts, D. J. Lee, J.K Archibald, and K.D. Lillywhite, “Dense Disparity Real-time Stereo Vision Algorithm for Resource Limited Systems,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21/10, p. 1547-1555, October 2011.
  3. B.J. Tippetts, D.J. Lee, and J.K Archibald, “Fast Correspondence of Unrectified Stereo Images using Genetic Algorithm and Spline Representation,” SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, vol. 7539, 75390W1-8, San Jose, CA, USA, January 17-21, 2010.
  4. P. Zhan, D.J. Lee, and R.W. Beard, “Solving Correspondence Problem Using 1-D Signal Matching, ” SPIE Optics East, Robotics Technologies and Architectures, Intelligent Robots and Computer Vision XXII, vol. 5608, p. 207-215, Philadelphia, PA, USA, October 25-28, 2004.
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