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Generalized Structure Tensor is an extension of the Cartesian Structure Tensor to Curvilinear coordinates. It finds the direction along which an image can undergo a translation with minimal error, measured in L2 norm amounting to total least squares sense, where the translation is along the curvilinear coordinates (instead of Cartesian). Among the curvilinear coordinates, locally orthogonal coordinates, are best studied.[1]
The Generalized structure tensor can be used as an alternative to Hough Transform in image processing and computer vision. The main differences comprise:
- Negative voting is allowed
- With one template multiple patterns belonging to the same family can be detected, because not nonly negative but also Complex Voting is allowed.
See also
References
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J. Bigun (1988). "Pattern recognition by detection of local symmetries". In E.S. Gelsema and L.N. Kanal (ed.). Pattern recognition and artificial intelligence. North-Holland. pp. 75–90.
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