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

Seungryong Kim, Dongbo Min, Bumsub Ham, Minh N. Do, Kwanghoon Sohn

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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.

Original languageEnglish
Article number7585122
Pages (from-to)1712-1729
Number of pages18
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
StatePublished - 1 Sep 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Dense correspondence
  • descriptor
  • edge-aware filtering
  • multi-modal
  • multi-spectral


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