Shape Matching Stereo
Vision for Resource
| 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
|| David and Deborah
Beau Tippetts and Pengcheng Zhan
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.
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.
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,
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,
image to view.)