title: DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation.
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.
authors.forename: Seungryong
authors.initials: S
authors.lastname: Kim
authors.forename: Dongbo
authors.initials: D
authors.lastname: Min
authors.forename: Bumsub
authors.initials: B
authors.lastname: Ham
authors.forename: Minh N
authors.initials: MN
authors.lastname: Do
authors.forename: Kwanghoon
authors.initials: K
authors.lastname: Sohn