Analysis of color space and similarity measure impact on stereo block matching

The impact of color space and similarity measure on complexity, speed, and performance of stereo matching is especially important to applications adopting stereo vision. This work analyzed the complexity of several most commonly considered color space and similarity measure. In addition, the execution speed and performance of color space and similarity measure combination are also compared on the same basis. The comparison result suggests that the Y-only rank provides the best combination under speed and performance trade-off.

[1]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[2]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Peter I. Corke,et al.  Quantitative Evaluation of Matching Methods and Validity Measures for Stereo Vision , 2001, Int. J. Robotics Res..

[4]  Won S. Kim,et al.  Performance Analysis and Validation of a Stereo Vision System , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Sheng Fu,et al.  Simultaneous Localization and Mapping for Autonomous Mobile Robots Using Binocular Stereo Vision System , 2007, 2007 International Conference on Mechatronics and Automation.

[6]  Dariu Gavrila,et al.  Real-time dense stereo for intelligent vehicles , 2006, IEEE Transactions on Intelligent Transportation Systems.

[7]  Masahiro Yokomichi,et al.  Stereo Correspondence Using Color Based on Competitive-cooperative Neural Networks , 2005, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05).

[8]  Tsukasa Ogasawara,et al.  Combining Fixed Stereo and Active Monocular Cameras into a Platform for Security Applications , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[9]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Paolo Grisleri,et al.  Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision , 2007, IEEE Transactions on Intelligent Transportation Systems.