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Juan Ruiz-Alzola, Ron Kikinis, and Carl-Fredrik Westin. 2001. “Detection of Point Landmarks in Multidimensional Tensor Data”. Signal Processing, 81, 10, Pp. 2243-47.
This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.Last updated on 02/24/2023