24 November 2014 Research on imaging ranging algorithm base on constraint matching of trinocular vision
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Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93011T (2014) https://doi.org/10.1117/12.2072386
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China
Abstract
Binocular stereo vision is a common passive ranging method, which directly simulates the approach of human visual. It can flexibly measure the stereo information in a complex condition. However there is a problem that binocular vision ranging accuracy is not high , one of the reasons is the low precision of the stereo image pairs matching . In this paper, based on trinocular vision imaging ranging algorithm of constraint matching, we use trinocular visual ranging system which is composed of three parallel placed cameras to image and achieve distance measurement of the target. we use calibration method of Zhang to calibrate the cameras, firstly, the three cameras are calibrated respectively, then using the results to get three groups binocular calibration. Thereby the relative position information of each camera are obtained. The using of the information obtained by the third camera can reduce ambiguity of corresponding points matching in a Binocular camera system. limiting search space by the epipolar constraint and improve the matching speed, filtering the distance information , eliminate interference information which brings by the feature points on the prospect and background to obtain a more accurate distance result of target. Experimental results show that, this method can overcome the limitations of binocular vision ranging , effectively improving the range accuracy.
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Pan Ye, Li Li, Wei-Qi Jin, Yu-tong Jiang, "Research on imaging ranging algorithm base on constraint matching of trinocular vision", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011T (24 November 2014); doi: 10.1117/12.2072386; https://doi.org/10.1117/12.2072386
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