Open AccessRobust local stereo matching under varying radiometric conditions
Author(s) -
Qu Yufu,
Jiang Jixiang,
Deng Xiangjin,
Zheng Yanhong
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0117
Subject(s) - artificial intelligence , matching (statistics) , computer science , computer vision , a priori and a posteriori , stereopsis , maximum a posteriori estimation , pattern recognition (psychology) , window (computing) , image (mathematics) , robustness (evolution) , mathematics , maximum likelihood , statistics , philosophy , biochemistry , chemistry , epistemology , gene , operating system
The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY descriptor and Census filtering. Then, a Census‐based cost aggregation with a self‐adaptive window is performed. Finally, the maximum a‐posteriori estimation is carried out to compute the disparity. The authors’ algorithm is compared with both local and global stereo matching algorithms (NLCA, ELAS, ANCC, AdaptWeight and CSBP) by using Middlebury datasets. The results show that the proposed algorithm achieves high‐accuracy dense disparity estimations and is more robust to radiometric differences between input images than other algorithms.