Fast multi-spectral image registration based on a statistical learning technique
Taeyoung Kim, Myungjin Choi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Statistical learning techniques have been used to dramatically speed-up keypoint matching and image registration. However, they are rarely applied to multi-spectral images. Statistical learning techniques regard various intensities as distinctive patterns. Thus, corresponding features extracted from multi-spectral images are recognized as different patterns, because the features have different intensity characteristics. In order to overcome this problem, we propose a novel statistical learning method that can be extended to multi-spectral images. The proposed approach obtains responses from multiple classifiers that are trained with well-registered multi-spectral images, in contrast to earlier approaches using one classifier. The responses of corresponding features can be similarly characterized as being of the same class even though the intensities of the corresponding features are quite different. The experimental results show that our method provides good performance on multi-spectral image registration compared to current methods.