Dynamic search range using sparse disparity map for fast stereo matching

In this paper, we have suggested a dynamic search range method to reduce a matching cost of the local stereo matching process basing on the sparse representation theory. The suggested method analyzes sparse disparity map, generating the valid search range information and then using the analyzed information skip the region where the matching blocks are not likely to be found in a dense disparity map acquisition step. Also considering for the solution of accuracy decline, we have offered some parameters to adjust speed and accuracy trade-off. Through experimental result, we have showed that the suggested algorithm reduces computation time 40% to 70% with a acceptable accuracy loss. The proposed algorithm can be applied to any local stereo matching algorithm. Also our algorithm is designed to be hardware friendly by lowering the hardware resource requirement and memory bandwidth.

[1]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Gruia-Catalin Roman,et al.  A parallel algorithm for incremental stereo matching on SIMD machines , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[3]  Shan Fu,et al.  Stereovision-Based Object Segmentation for Automotive Applications , 2005, EURASIP J. Adv. Signal Process..

[4]  KweonIn So,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006 .

[5]  Yunde Jia,et al.  A miniature stereo vision machine (MSVM-III) for dense disparity mapping , 2004, ICPR 2004.

[6]  Lei Chen,et al.  A Parallel Reconfigurable Architecture for Real-Time Stereo Vision , 2009, 2009 International Conference on Embedded Software and Systems.

[7]  Don Ray Murray,et al.  Using Real-Time Stereo Vision for Mobile Robot Navigation , 2000, Auton. Robots.

[8]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Tian-Sheuan Chang,et al.  Algorithm and Architecture of Disparity Estimation With Mini-Census Adaptive Support Weight , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

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

[11]  A. C. Sonmez,et al.  FPGA design and implementation of a real-time stereo vision system , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[12]  Ines Ernst,et al.  Mutual Information Based Semi-Global Stereo Matching on the GPU , 2008, ISVC.

[13]  Jun Dong Cho,et al.  Hardware mass object analyser implementation for stereo camera , 2011, Signal Processing Algorithms, Architectures, Arrangements, and Applications SPA 2011.

[14]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.