Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 민동보 | * |
dc.date.accessioned | 2019-11-19T16:30:22Z | - |
dc.date.available | 2019-11-19T16:30:22Z | - |
dc.date.issued | 2017 | * |
dc.identifier.issn | 0162-8828 | * |
dc.identifier.issn | 1939-3539 | * |
dc.identifier.other | OAK-25979 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/251893 | - |
dc.description.abstract | Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence between multi-modal or multi-spectral images still remains unsolved due to their challenging photometric and geometric variations. In this paper, we propose a novel dense descriptor, called dense adaptive self-correlation (DASC), to estimate dense multi-modal and multi-spectral correspondences. Based on an observation that self-similarity existing within images is robust to imaging modality variations, we define the descriptor with a series of an adaptive self-correlation similarity measure between patches sampled by a randomized receptive field pooling, in which a sampling pattern is obtained using a discriminative learning. The computational redundancy of dense descriptors is dramatically reduced by applying fast edge-aware filtering. Furthermore, in order to address geometric variations including scale and rotation, we propose a geometry-invariant DASC (GI-DASC) descriptor that effectively leverages the DASC through a superpixel-based representation. For a quantitative evaluation of the GI-DASC, we build a novel multi-modal benchmark as varying photometric and geometric conditions. Experimental results demonstrate the outstanding performance of the DASC and GI-DASC in many cases of dense multi-modal and multi-spectral correspondences. | * |
dc.language | English | * |
dc.publisher | IEEE COMPUTER SOC | * |
dc.subject | Dense correspondence | * |
dc.subject | descriptor | * |
dc.subject | multi-spectral | * |
dc.subject | multi-modal | * |
dc.subject | edge-aware filtering | * |
dc.title | DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation | * |
dc.type | Article | * |
dc.relation.issue | 9 | * |
dc.relation.volume | 39 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 1712 | * |
dc.relation.lastpage | 1729 | * |
dc.relation.journaltitle | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | * |
dc.identifier.doi | 10.1109/TPAMI.2016.2615619 | * |
dc.identifier.wosid | WOS:000406840800002 | * |
dc.author.google | Kim, Seungryong | * |
dc.author.google | Min, Dongbo | * |
dc.author.google | Ham, Bumsub | * |
dc.author.google | Do, Minh N. | * |
dc.author.google | Sohn, Kwanghoon | * |
dc.contributor.scopusid | 민동보(7201669172) | * |
dc.date.modifydate | 20240322133757 | * |