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- W2080762661 abstract "This paper addresses the problem of the local scale parameter selection for recognition techniques based on Gaussian derivatives. Patterns are represented in a local subspace or feature space of which each dimension is a scale normalized receptive field (a unit composed of normalized Gaussian-based filters). Scale invariance is obtained by automatic selection of an appropriate local scale [Lindeberg, Int. J. Comput. Vision 30 (2) (1998) 79] and followed by normalization of the receptive field to the appropriate scale. The power of a scale equivariant feature space is shown by experimental results for point correspondences in images of different scales and the recognition of objects under scale variations. The approach is validated with the results of experiments in the discrimination of three goldfish species. During the training phase, each goldfish is isolated from the others and its behavior is uncontrolled. Data are structurally represented in a feature space for the recognition of static deformable object configurations. A statistical description is used for object recognition based on temporal activity. Recognition is processed using the static configuration of fish and their temporal activity." @default.
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- W2080762661 date "2001-06-01" @default.
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- W2080762661 title "Recognizing goldfish? or Local scale selection for recognition techniques" @default.
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- W2080762661 doi "https://doi.org/10.1016/s0921-8890(01)00124-5" @default.
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