A Multi-Modal Edge Consistency Metric Based on Regression Robustness of Truncated SVD

In this paper, we propose a novel edge consistency metric for multi-modal correspondence. It is based on a novel observation on image truncated SVD (singular value decomposition) termed regression robustness, which describes the fact that, a good approximation from image truncated SVD can be inherited even if the eigen-images change due to expansion and channel-dependent offsets. Compared to state-of-the-arts, multi-modal edge consistency metric can simultaneously handle multiple images with complex modality changes, including local variation, gradient reverse, intensity order change, and texture loss. Its complexity is almost linear to pixel number. Remarkable accuracies have been achieved in experiments.