A pixel pair-based encoding pattern for stereo matching via an adaptively weighted cost

Stereo matching, which is a key problem in computer vision, faces the challenge of radiometric distortions. Most of the existing stereo matching methods are based on simple matching cost algorithms and appear the problem of mismatch under radiometric distortions. It is necessary to improve the robustness and accuracy of matching cost algorithms. A novel encoding pattern is proposed for stereo matching. In the proposed encoding pattern, each of the matching windows in the grey image and gradient images is divided into several isoline-like sets with different radii. Then, pixel pairs are defined in the isoline-like sets. An encoding function is used to decide the relative order between the two pixels in each pixel pair. To apply the pattern for matching cost computation and enhance the matching accuracy, an adaptively weighted cost is designed that is related to the isoline-like sets. Experiments are conducted on the Middlebury and KITTI data sets to show the validity of the proposed method under severe radiometric distortions. Also, the comparisons with some widely used methods are made in the experiments to illustrate the advantage of the proposed method.

[1]  Jaeseung Lim,et al.  Patchmatch-Based Robust Stereo Matching Under Radiometric Changes , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yangzhou Gan,et al.  Segment-Based Disparity Refinement With Occlusion Handling for Stereo Matching , 2019, IEEE Transactions on Image Processing.

[3]  Menglong Yang,et al.  Learning both matching cost and smoothness constraint for stereo matching , 2018, Neurocomputing.

[4]  Xingrui Yu,et al.  Stereo Matching via Dual Fusion , 2018, IEEE Signal Processing Letters.

[5]  Li Zhang,et al.  PMSC: PatchMatch-Based Superpixel Cut for Accurate Stereo Matching , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Benjamin W. Wah,et al.  Fundamental Principles on Learning New Features for Effective Dense Matching , 2018, IEEE Transactions on Image Processing.

[7]  Takeshi Naemura,et al.  Continuous 3D Label Stereo Matching Using Local Expansion Moves , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Kyoung Mu Lee,et al.  Look Wider to Match Image Patches With Convolutional Neural Networks , 2017, IEEE Signal Processing Letters.

[9]  Lei Zhang,et al.  Joint Image Denoising and Disparity Estimation via Stereo Structure PCA and Noise-Tolerant Cost , 2017, International Journal of Computer Vision.

[10]  Kate Saenko,et al.  Guest Editorial: Image and Language Understanding , 2017, International Journal of Computer Vision.

[11]  Cheng Zhang,et al.  Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Jae Wook Jeon,et al.  Fuzzy Encoding Pattern for Stereo Matching Cost , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Raquel Urtasun,et al.  Efficient Deep Learning for Stereo Matching , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Jae Wook Jeon,et al.  Matching cost function using robust soft rank transformations , 2016, IET Image Process..

[15]  Yann LeCun,et al.  Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches , 2015, J. Mach. Learn. Res..

[16]  Qingxiong Yang,et al.  Stereo Matching Using Tree Filtering , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jae Wook Jeon,et al.  Support Local Pattern and its Application to Disparity Improvement and Texture Classification , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Weiming Dong,et al.  Segment-tree based cost aggregation for stereo matching with enhanced segmentation advantage , 2013, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Sang Uk Lee,et al.  Joint Depth Map and Color Consistency Estimation for Stereo Images with Different Illuminations and Cameras , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

[21]  Sang Uk Lee,et al.  Robust Stereo Matching Using Adaptive Normalized Cross-Correlation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  H. Hirschmüller.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[27]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[28]  Takeo Kanade,et al.  Development of a video-rate stereo machine , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

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