Structure tensor: Difference between revisions

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In mathematics, the '''structure [[tensor]]''', also referred to as the '''second-moment matrix''', is a [[matrix (mathematics)|matrix]] derived from the [[gradient]] of a [[function (mathematics)|function]]. It describes the distribution of the gradient in a specified neighborhood around a point and makes the information invariant respectto the observing coordinates<!-- Example: if you have a 2D image with two components storing the gradient direction and a Gaussian blur is performed separately on each component, the result will be ill-formed (specially for the directions were vector orientations flip). On the other hand if the blur is performed component-wise on a 2x2 structure tensor the main eigenvector (scaled by its eigenvalue) will properly represent the gradient. -->. The structure tensor is often used in [[image processing]] and [[computer vision]].<ref name=bigun86>

J. Bigun and G. Granlund (1986), ''Optimal Orientation Detection of Linear Symmetry''. Tech. Report LiTH-ISY-I-0828, Computer Vision Laboratory, Linkoping University, Sweden 1986; Thesis Report, Linkoping studies in science and technology No. 85, 1986.

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