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DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation

Title
DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation
Authors
Kim, SeungryongMin, DongboHam, BumsubDo, Minh N.Sohn, Kwanghoon
Ewha Authors
민동보
Issue Date
2017
Journal Title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN
0162-8828JCR Link

1939-3539JCR Link
Citation
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE vol. 39, no. 9, pp. 1712 - 1729
Keywords
Dense correspondencedescriptormulti-spectralmulti-modaledge-aware filtering
Publisher
IEEE COMPUTER SOC
Indexed
SCI; SCIE; SCOPUS WOS
Document Type
Article
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.
DOI
10.1109/TPAMI.2016.2615619
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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