We present a novel descriptor, called deep self-correlation (DSC), designed for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. Motivated by local self-similarity (LSS), we formulate a novel descriptor by leveraging LSS in a deep architecture, leading to better discriminative power and greater robustness to non-rigid image deformations than state-of-the-art descriptors. The DSC first computes self-correlation surfaces over a local support window for randomly sampled patches, and then builds hierarchical self-correlation surfaces by performing an average pooling within a deep architecture. Finally, the feature responses on the self-correlation surfaces are encoded through a spatial pyramid pooling in a circular configuration. In contrast to convolutional neural networks (CNNs) based descriptors, the DSC is trainingfree, is robust to cross-modal imaging, and can be densely computed in an efficient manner that significantly reduces computational redundancy. The state-of-the-art performance of DSC on challenging cases of cross-modal image pairs is demonstrated through extensive experiments.
CITATION STYLE
Kim, S., Min, D., Lin, S., & Sohn, K. (2016). Deep self-correlation descriptor for dense cross-modal correspondence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9912 LNCS, pp. 679–695). Springer Verlag. https://doi.org/10.1007/978-3-319-46484-8_41
Mendeley helps you to discover research relevant for your work.