A Modified Census Transform Based on the Neighborhood Information for Stereo Matching Algorithm

Census transform is a non-parametric local transform. Its weakness is that the results relied on the center pixel too much. This paper proposes a modified Census transform based on the neighborhood information for stereo matching. By improving the classic Census transform, the new technique utilizes more bits to represent the differences between the pixel and its neighborhood information. The result image of the modified Census transform has more detailed information at depth discontinuity. After stereo correspondence, sub-pixel interpolation and the disparity refinement, a better dense disparity map can be obtained. The experiments present that the proposed algorithm has simple mechanism and strong robustness. It can improve the accuracy of matching and is applicable to hardware systems.

[1]  Haibin Yu,et al.  Real-time stereo vision system using adaptive weight cost aggregation approach , 2011, EURASIP J. Image Video Process..

[2]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[3]  Zhu Shi-qiang.  Mutual information based non-parametric transform stereo matching algorithm , 2011 .

[4]  Kristian Ambrosch,et al.  A miniature embedded stereo vision system for automotive applications , 2010, 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel.

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

[6]  Kristian Ambrosch,et al.  Accurate hardware-based stereo vision , 2010, Comput. Vis. Image Underst..

[7]  Ruigang Yang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007, International Journal of Computer Vision.

[8]  Zhigeng Pan,et al.  Real-time stereo matching based on fast belief propagation , 2011, Machine Vision and Applications.

[9]  Emanuele Trucco,et al.  Efficient stereo with multiple windowing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Hong Jeong,et al.  Occlusion detection and filling in disparity map for multiple view synthesis , 2012, 2012 8th International Conference on Computing and Networking Technology (INC, ICCIS and ICMIC).