MST Stereo Matching Algorithm Based on Weighted Median Filtering

doi:10.3969/j.issn.1006-2475.2018.05.004

Abstract

Abstract: The traditional Minimum Spanning Tree(MST) stereo matching algorithm is insensitive to exture-less regions and occlusion areas. Moreover, the median filter of the minimum spanning tree stereo matching algorithm can eliminate the noise, but it cannot eliminate the edge blur. This paper presents an improved algorithm to overcome these limitations. Firstly, because the cost discrimination of the minimum spanning tree matching is not enough high, the matching cost of the new minimum spanning tree is studied and it can reduce the mismatching of the insensitive region. Secondly, weighted median filtering is used in post-processing to improve image boundaries. The experimental results show that the improved matching algorithm and the weighted median filtering algorithm are used in the minimum spanning tree stereo matching algorithm, and the average false matching rate is 6.9% in the Middlebury data set. The algorithm is superior to minimum spanning tree in Middlebury and KITTI.

Key words: minimum spanning tree, exture-less, occlusion areas, median filtering, match cost, weighted median filter

CLC Number:

TP242.6

ZHAO Da-cheng, XU Dao-yun. MST Stereo Matching Algorithm Based on Weighted Median Filtering[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.05.004.

References

［1］     Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms［J］. International Journal of Computer Vision, 2002,47(1-3):7-42. 
［2］ Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002,23(11):1222-1239.
［3］    Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts?〖KG-*3〗［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004,26(2):147-159. 
［4］ Yang Qingxiong, Wang L, Ahuja N. A constant-space belief propagation algorithm for stereo matching［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2010:1458-1465.
［5］ Zhang Ke, Lu Jiangbo, Lafruit G. Cross-based local stereo matching using orthogonal integral images［J］. IEEE Transactions on Circuits & Systems for Video Technology, 2009,19(7):1073-1079.
［6］ Yoon K J, Kweon I S. Adaptive support-weight approach for correspondence search［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2006,28(4):650-656.
［7］   Hirschmüller H, Innocent P R, Garibaldi J. Real-time correlation-based stereo vision with reduced border errors［J］. International Journal of Computer Vision, 2002,47(1-3):229-246.
［8］ Yang Qingxiong. A non-local cost aggregation method for stereo matching［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2012:1402-1409.
［9］ Ma Ziyang, He Kaiming, Wei Yichen, et al. Constant time weighted median filtering for stereo matching and beyond［C］// IEEE International Conference on Computer Vision. 2013:49-56.
［10］Li Linchen, Yu Xin, Zhang Shunli, et al. 3D cost aggregation with multiple minimum spanning trees for stereo matching［J］. Applied Optics, 2017,56(12):3411-3420.
［11］Bleyer M, Rhemann C, Rother C. PatchMatch stereo-stereo matching with slanted support windows［C］// British Machine Vision Conference. 2011:14.1-14.11.
［12］Hamzah R A, Ibrahim H, Hassan A H A. Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation［J］. Journal of Visual Communication & Image Representation, 2016,42(1):145-160.
［13］Perreault S, Hebert P. Median filtering in constant time［J］. IEEE Transactions on Image Processing, 2007,16(9):2389-2394.
［14］Rhemann C, Hosni A, Bleyer M, et al. Fast cost-volume filtering for visual correspondence and beyond［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2011:3017-3024.

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