Stereo Matching Algorithm Based on Wavelet Transform and Joint Selection

Aiming at the fact that the accuracy and speed of the existing local stereo matching method can not meet the requirements of practical application at the same time, this paper proposes a stereo matching algorithm by means of wavelet transform and image features. Firstly, the image is wavelet transformed to obtain high-dimensional information of the image, the image features are combined for matching cost calculation. Secondly, in terms of parallax correction, a joint selection mechanism is proposed. When small-area joint selection is performed, the edge detection is used to refine the selection area, and the object edge information is fully limited as a joint selection area , ensuring that the algorithm can have an exact match also in a depth discontinuity area . After the 2014 dataset test provided by the Middlebury test platform, the algorithm has higher matching accuracy and faster speed than some excellent algorithms such as SSWTAD algorithm and Adapt Weight algorithm .

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

[2]  Zhang Huayu,et al.  Binarization of degraded document image based on contrast enhancement , 2016, 2016 35th Chinese Control Conference (CCC).

[3]  Xin Huang,et al.  Binarization of degraded document images based on contrast enhancement , 2018, International Journal on Document Analysis and Recognition (IJDAR).

[4]  Guijin Wang,et al.  High-accuracy stereo matching based on adaptive ground control points. , 2015, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[5]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Ki-Dong Chung,et al.  Visual stereo matching combined with intuitive transition of pixel values , 2016, Multimedia Tools and Applications.

[7]  Qingxiong Yang,et al.  A non-local cost aggregation method for stereo matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Liming Chen,et al.  A Fast Trilateral Filter-Based Adaptive Support Weight Method for Stereo Matching , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Guijin Wang,et al.  High-Accuracy Stereo Matching Based on Adaptive Ground Control Points , 2015, IEEE Transactions on Image Processing.

[10]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Liming Chen,et al.  Depth edge based trilateral filter method for stereo matching , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[12]  Shiqiang Zhu,et al.  Convolutional neural network based deep conditional random fields for stereo matching , 2016, J. Vis. Commun. Image Represent..

[13]  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.

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