Image registration is a key component in remote sensing image processing. In this paper, we present a remote sensing image registration method by incorporating spatial restraint based on moment invariants and fast generalized fuzzy clustering. Seven moment invariants are extracted as features of objects obtained by the fast generalized fuzzy c-means (FGFCM) algorithm. The objects are matched through these features. Then, we detect the keypoints in corresponding matching regions.Through the spatial restraint, the outliers are removed and the correct matches are increased. The proposed algorithm is evaluated on multi-spectral images, multi-temporal images, and multi-sensor images. Extensive experimental studies prove that the proposed algorithm is promising.
Recent advancement in computer hardware and software have led to an increasing amount of 3D models. Furthermore, the rapid development of WWW enables the access to 3D models constructed by people all over the world. This paper surveyed the background, state-of-theart review and progress of content-based 3D shape retrieval, and analyzed and described several topics, including the preprocessing, coordinates standardization, feature extraction, similarity matching, query interface and existing typical 3D model search engines. Also, we classified the existing 3D shape similarity measures and focus on several 3D model similarity measures. Finally, advantages and disadvantages of these methods are discussed and future works are proposed.
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