DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
This addresses a fundamental task in computer vision for applications like image registration, but it is incremental as it builds on existing descriptor methods with specific adaptations.
The paper tackles the problem of estimating dense correspondences between multi-modal or multi-spectral images, which is challenging due to photometric and geometric variations, by proposing the DASC and GI-DASC descriptors, resulting in outstanding performance demonstrated in experiments.
Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence in 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 multi-modal and multi-spectral dense 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 multi-modal and multi-spectral dense correspondences.