CASIA OpenIR  > 模式识别国家重点实验室  > 三维可视计算
High accuracy correspondence field estimation via MST based patch matching
Zhang, Feihu; Xu, Shibiao; Zhang, Xiaopeng
发表期刊MULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2020-01-28
页码19
通讯作者Xu, Shibiao(shibiao.xu@ia.ac.cn) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn)
摘要This paper presents an effective framework for correspondence field estimation. The core idea is to construct pixel-level and superpixel-level patch matching to achieve high accuracy estimation as well as fast speed computation. To this end, a hybrid edge-preserving supported weighting approach is first developed, which contributes to better performance on the pixel level, especially on those in the regions of fine structures. Then, a local Minimum Spanning Tree (MST) is constructed to describe regions and develop the adaptive smooth penalty weights, so that the over-patching in large textureless regions can be effectively avoided. In addition, the MST is further extended to handle occlusions in way of edge preserving strategy. Finally, all the above treatments are collected into an optimization model where the objective function is developed in terms of Markov Random Filed (MRF). In computation, a fast yet efficient iterative optimization strategy is developed. Our approach achieves favorable place on optical flow benchmark, which locates at the top two and top four for endpoint error and angular error evaluations among more than 130 approaches listed in the webpage.
关键词Optical flow Minimum spanning tree PatchMatch
DOI10.1007/s11042-020-08633-y
关键词[WOS]ADAPTIVE SUPPORT ; OPTICAL-FLOW ; STEREO
收录类别SCI
语种英语
资助项目National Key R&D Program of China[2018YFB2100602] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61571046] ; National Key R&D Program of China[2018YFB2100602] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61571046]
项目资助者National Key R&D Program of China ; National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000515728500005
出版者SPRINGER
七大方向——子方向分类三维视觉
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/38321
专题模式识别国家重点实验室_三维可视计算
通讯作者Xu, Shibiao; Zhang, Xiaopeng
作者单位Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Zhang, Feihu,Xu, Shibiao,Zhang, Xiaopeng. High accuracy correspondence field estimation via MST based patch matching[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2020:19.
APA Zhang, Feihu,Xu, Shibiao,&Zhang, Xiaopeng.(2020).High accuracy correspondence field estimation via MST based patch matching.MULTIMEDIA TOOLS AND APPLICATIONS,19.
MLA Zhang, Feihu,et al."High accuracy correspondence field estimation via MST based patch matching".MULTIMEDIA TOOLS AND APPLICATIONS (2020):19.
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