| Feature detection |
|---|
| Edge detection |
| Corner detection |
| Blob detection |
| Ridge detection |
| Hough transform |
| Structure tensor |
| Affine invariant feature detection |
| Feature description |
| Scale space |
In mathematics, the structure tensor, also referred to as the second-moment matrix, is a matrix derived from the gradient of a function. It describes the distribution of the gradient in a specified neighborhood around a point and makes the information invariant to the observing coordinates. The structure tensor is often used in image processing and computer vision.[1][2][3]
- ^ 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.
- ^ J. Bigun & G. Granlund (1987). "Optimal Orientation Detection of Linear Symmetry". First int. Conf. on Computer Vision, ICCV, (London). Piscataway: IEEE Computer Society Press, Piscataway. pp. 433–438.
- ^ H. Knutsson (1989). "Representing local structure using tensors". Proceedings 6th Scandinavian Conf. on Image Analysis. Oulu: Oulu University. pp. 244–251.