ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 59-66, 2016
03 Jun 2016
Han Hu1,2, Chongtai Chen2, Bo Wu1, Xiaoxia Yang3, Qing Zhu2,4, and Yulin Ding2 1Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hum Hong, Kowloon, Hong Kong
2Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University, Gaoxin West District, Chengdu, China
3College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
4Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan, China
Keywords: Dense Image Matching, Texture aware, Census Transform, Local Ternary Pattern, SGM, Matching Cost Abstract. Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.
Conference paper (PDF, 1197 KB)

Citation: Hu, H., Chen, C., Wu, B., Yang, X., Zhu, Q., and Ding, Y.: TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 59-66, doi:10.5194/isprs-annals-III-3-59-2016, 2016.

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