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3D Composite-Feature DetectorsMethodology for data-driven synthesis of composite feature-detectors using a decomposition-integration strategy. The image is decomposed into a set of elementary frequency features. To obtain composite features, simple features are integrated by means of unsupervised clustering. The criterion for binding is based on the concept of Phase Congruence and is reflected using image dissimilarity measures. It has been applied in different tasks of processing and analysis of 3D and 2D+t data:
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Feature Preserving Diffusion FilteringAnisotropic filter with a diffusivity tensor that depends on image features, specifically surfaces and corners. Applications
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Superquadric Model MatchingObject identification and rough approximation to its surface by matching with an a priori model of a superquadric with global deformations. A priori models are generated by fitting to sample points of a training surface using a GA algorithm. Model matching is accomplished by optimization of affine transformation parameters to fit image surface patches:
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