Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65010
Title: Texture-aware dense image matching using ternary census transform
Authors: Hu, H
Chen, C
Wu, BO 
Yang, X
Zhu, Q
Ding, Y
Keywords: Dense image matching
Texture aware
Census transform
Local ternary pattern
SGM
Matching cost
Issue Date: 2016
Publisher: Copernicus Publications
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2016, v. III-3, p. 59-66 How to cite?
Journal: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 
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.
Description: XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic
URI: http://hdl.handle.net/10397/65010
ISSN: 2194-9042 (print)
2194-9050 (online)
DOI: 10.5194/isprs-annals-III-3-59-2016
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