Dense Stereo Matching Based on the Directional Local Binary Pattern

Document Type : Research Article

Authors

Department of Electrical Engineering, Tafresh University, Tafresh, Iran

Abstract

New applications such as 3D graphics, 3D displays, and image-based modeling have made stereo vision an active research area in recent years. In dense disparity map estimation, which is a basic problem in stereo vision, using two left and right images taken from a scene from two different positions, the disparity of each pixel of the reference image is determined (meaning determining each pixel with how displacement is appeared in the other image). Based on the disparity value, the depth of each pixel in scene is simply determined. For dense disparity map estimation, local stereo matching methods are simpler and faster than global methods, and therefore suitable for real time applications. In these methods, defining proper window which aggregate intensity pattern as well as keeping disparity consistency in all the window area, is an important challenge. To overcome this challenge, the idea of directional multiple window has been proposed in the previous researches. On the other hand, local binary patterns have considerable success in pattern recognition applications, while computationally simple. Therefore, the idea of using local binary pattern in a directional multiple window arrangement is proposed for dense stereo matching in this paper. Experimental results on standard stereo images show the better performance of the proposed method with respect to other proposed binary descriptors

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References

[1] D. Scharstein, R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International journal of computer vision, 47(1) (2002) 7-42.
[2] A.M. Fotouhi, R. Abolghasem, Optimizing Disparity Candidates Space in Dense Stereo Matching, aut journal of modeling and simulation, 43(2) (2011) 43-51.
[3] T. Kanade, M. Okutomi, A stereo matching algorithm with an adaptive window: Theory and experiment, IEEE transactions on pattern analysis and machine intelligence, 16(9) (1994) 920-932.
[4] O. Veksler, Stereo correspondence with compact windows via minimum ratio cycle, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12) (2002) 1654- 1660.
[5] A. Fusiello, V. Roberto, E. Trucco, Efficient stereo with multiple windowing, in: Proceedings of IEEE Computer Society conference on computer vision and pattern recognition, IEEE, 1997, pp. 858-863.
[6] Y. Xu, D. Wang, T. Feng, H.-Y. Shum, Stereo computation using radial adaptive windows, in: 2002 International Conference on Pattern Recognition, IEEE, 2002, pp. 595-598.
[7] A.F. Bobick, S.S. Intille, Large occlusion stereo, International Journal of Computer Vision, 33(3) (1999) 181-200.
[8] S.B. Kang, R. Szeliski, J. Chai, Handling occlusions in dense multi-view stereo, in: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, IEEE, 2001, pp. I-I.
[9] H. Hirschmüller, P.R. Innocent, J. Garibaldi, Real-time correlation-based stereo vision with reduced border errors, International Journal of Computer Vision, 47(1) (2002) 229-246.
[10] O. Veksler, Fast variable window for stereo correspondence using integral images, in: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., IEEE, 2003, pp. I-I.
[11] K.-J. Yoon, I.S. Kweon, Adaptive support-weight approach for correspondence search, IEEE transactions on pattern analysis and machine intelligence, 28(4) (2006) 650-656.
[12] A. Hosni, M. Bleyer, M. Gelautz, C. Rhemann, Local stereo matching using geodesic support weights, in: 2009 16th IEEE International Conference on Image Processing (ICIP), IEEE, 2009, pp. 2093-2096.
[13] D. Huang, C. Shan, M. Ardabilian, Y. Wang, L. Chen, Local binary patterns and its application to facial image analysis: a survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6) (2011) 765-781.
AUT Journal of Electrical Engineering
Volume 55, Issue 1
2023
Pages 125-130

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