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|a 10.1109/TPAMI.2016.2615619
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|a eng
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| 100 |
1 |
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|a Kim, Seungryong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a DASC
|b Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation
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|c 2017
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|a Text
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|a Date Completed 15.11.2018
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|a Date Revised 15.11.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Min, Dongbo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ham, Bumsub
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Do, Minh N
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sohn, Kwanghoon
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 9 vom: 01. Sept., Seite 1712-1729
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|g volume:39
|g year:2017
|g number:9
|g day:01
|g month:09
|g pages:1712-1729
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|u http://dx.doi.org/10.1109/TPAMI.2016.2615619
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