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We consider the problem of simultaneously registering several images to a 3D model. We propose a global approach based on mutual information that extends previous methods to incorporate the color, and does not require segmentation or feature extraction. We give a stochastic model for joint optimization of multiple image-to-model alignment and we propose a(More)
Clustering algorithms are intensively used in the image analysis field in compression, segmentation, recognition and other tasks. In this work we present a new approach in clustering vector datasets by finding a good order in the set, and then applying an optimal segmentation algorithm. The algorithm heuristically prolongs the optimal scalar quantization(More)
Intensity based registration methods, such as the mutual information (MI), do not commonly consider the spatial geometric information and the initial correspondences are uncertainty. In this paper, we present a novel approach for achieving highly-automatic 2D/3D image registration integrating the advantages from both entropy MI and spatial geometric(More)
In the process of digitizing the geometry and appearance of 3D objects, texture registration is a necessary step that solves the 2D–3D mapping between the 2D texture images and the 3D geometric model. For evaluation of texture registration with ground truth, accurate datasets can be obtained with a complex setup consisting of calibrated geometry and texture(More)
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