文献类型：期刊文献

英文题名：Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement

作者：Zhan, Yunlong[1];Gu, Yuzhang[1];Huang, Kui[2];Zhang, Cheng[1];Hu, Keli[3]

机构：[1]Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Wireless Sensor Network & Commun, Shanghai 200050, Peoples R China;[2]Univ Sci & Technol China, Hefei 230026, Peoples R China;[3]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China

年份：2016

卷号：26

期号：9

起止页码：1632

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:000384078400005)、、EI(收录号：20163702808907)、Scopus(收录号：2-s2.0-84986587791)、WOS

基金：This work was supported by the Strategic Priority Research Program through the Chinese Academy of Sciences under Grant XDA06020300. This paper was recommended by Associate Editor C. Zhang.

语种：英文

外文关键词：Double-RGB gradient; four-direction propagation; guidance image; lightweight census transform; stereo matching

外文摘要：Stereo matching is a challenging problem, and high-accuracy stereo matching is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and 3-D reconstruction. Therefore, we present a novel image-guided stereo matching algorithm, which employs the efficient combined matching cost and multistep disparity refinement, to improve the accuracy of existing local stereo matching algorithms. Different from all the other methods, we introduce a guidance image for the whole algorithm. This filter-based guidance image is generated by extracting the enhanced information from the raw stereo image. The combined matching cost consists of the novel double-RGB gradient, the improved lightweight census transform, and the image color. This cost measurement is robust against image noise and textureless regions in computing the matching cost. Furthermore, a new systemic multistep refinement process, which includes outlier classification, four-direction propagation, leftmost propagation, and an exponential step filter, is proposed to remove the outliers in the raw disparity map. Experiments on the Middlebury benchmark demonstrate our algorithm's superior performance that it ranks first among the 158 submitted algorithms. Moreover, the proposed method is also robust on the 30 Middlebury data sets and the real-world Karlsruhe Institute of Technology and Toyota Technological Institute benchmark.