A Stereo Matching Algorithm Guided by Multiple Linear Regression

被引：0
|
作者：
Han X. [1 ,2 ]
Liu Y. [1 ,2 ]
Yang H. [1 ,2 ]
机构：
[1] College of Computer Science, Sichuan University, Chengdu
[2] National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 01期
关键词：
Cost aggregation; Fast weighting median filtering; Guided filter; Multiple linear regression; Stereo matching;
D O I：
10.3724/SP.J.1089.2019.17356
中图分类号：
学科分类号：
摘要：
In the stereo matching, the matching of edge pixels in depth image is one of the challenging problems of this technology. The local stereo matching based on the guided image filtering can protect the edge of the depth image and improve the matching precision and accelerate the speed, but this makes the image halo and also introduces a lot of noise in the edge area of the image. In this paper, ridge regression of guided filter is extended to multiple linear regression, and a stereo matching framework of multiple linear regression is proposed to extend the cost aggregation method. The framework is designed to better protect the edge of the depth image. To improve the matching accuracy of the disparity, the proposed algorithm sets up the multiple regression equation of the image pixel value and the gradient information as the variable. Then a weighted combination of cost aggregated values is carried out with the guidance filtering alone. At the same time, the concept of credibility is defined. It is described by the relationship between the minimum value and the minor value of the cost aggregation to avoid ambiguity when facing the disparity selection. The algorithm in this paper is tested on the Middlebury platform. The results show that the framework can effectively improve the precision and reduce the noise. Compared with some high performance algorithms, the algorithm can get high quality disparity map. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：84 / 93
页数：9
相关论文
共 37 条
  • [1] Scharstein D., Szeliski R., A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47, 1-3, pp. 7-42, (2002)
  • [2] Klaus A., Sormann M., Karner K., Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure, Proceedings of the 18th International Conference on Pattern Recognition, 3, pp. 15-18, (2006)
  • [3] Kolmogorov V., Zabih R., Computing visual correspondence with occlusions using graph cuts, Proceedings of the 8th IEEE International Conference on Computer Vision, pp. 508-515, (2001)
  • [4] Kordelas G.A., Alexiadis D.S., Daras P., Et al., Content-based guided image filtering, weighted semi-global optimization, and efficient disparity refinement for fast and accurate disparity estimation, IEEE Transactions on Multimedia, 18, 2, pp. 155-170, (2016)
  • [5] Zhang K., Lu J.B., Lafruit G., Cross-based local stereo matching using orthogonal integral images, IEEE Transactions on Circuits and Systems for Video Technology, 19, 7, pp. 1073-1079, (2009)
  • [6] Papenberg N., Bruhn A., Brox T., Et al., Highly accurate optic flow computation with theoretically justified warping, International Journal of Computer Vision, 67, 2, pp. 141-158, (2006)
  • [7] Brox T., Malik J., Large displacement optical flow: descriptor matching in variational motion estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 3, pp. 500-513, (2011)
  • [8] Hermann S., Vaudrey T., The gradient-a powerful and robust cost function for stereo matching, Proceedings of the 25th International Conference on Image and Vision Computing New Zealand, pp. 1-8, (2010)
  • [9] Baek E.T., Ho Y.S., Cost aggregation with guided image filter and superpixel for stereo matching, Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and ConferencePress, pp. 1-4, (2016)
  • [10] Hirschmuller H., Stereo processing by semiglobal matching and mutual information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 2, pp. 328-341, (2008)
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