| Feature detection |
|---|
| Edge detection |
| Corner detection |
| Blob detection |
| Ridge detection |
| Hough transform |
| Structure tensor |
| Affine invariant feature detection |
| Feature description |
| Scale space |
In image analysis, the generalized structure tensor (GST) is an extension of the Cartesian structure tensor to curvilinear coordinates.[1] It is mainly used to detect and to represent the "direction" parameters of curves, just as the Cartesian structure tensor detects and represents the direction in Cartesian coordinates. Curve families generated by pairs of locally orthogonal functions have been the best studied.
It is a widely known method in applications of image and video processing including computer vision, such as biometric identification by fingerprints,[2] and studies of human tissue sections.[3][4]
- ^ Bigun, J.; Bigun, T.; Nilsson, K. (December 2004). "Recognition by symmetry derivatives and the generalized structure tensor". IEEE Transactions on Pattern Analysis and Machine Intelligence. 26 (12): 1590–1605. doi:10.1109/TPAMI.2004.126. PMID 15573820. S2CID 602221.
- ^ Fronthaler, H.; Kollreider, K.; Bigun, J. (2008). "Local Features for Enhancement and Minutiae Extraction in Fingerprints". IEEE Transactions on Image Processing. 17 (3): 354–363. Bibcode:2008ITIP...17..354F. CiteSeerX 10.1.1.160.6312. doi:10.1109/TIP.2007.916155. PMID 18270124. S2CID 7119251.
- ^ O. Schmitt; H. Birkholz (2010). "Improvement in cytoarchitectonic mapping by combining electrodynamic modeling with local orientation in high-resolution images of the cerebral cortex". Microsc. Res. Tech. 74 (3): 225–243. doi:10.1109/TIP.2007.916155. PMID 18270124. S2CID 7119251.
- ^ O. Schmitt; M. Pakura; T. Aach; L. Homke; M. Bohme; S. Bock; S. Preusse (2004). "Analysis of nerve fibers and their distribution in histologic sections of the human brain". Microsc. Res. Tech. 63 (4): 220–243. doi:10.1002/jemt.20033. PMID 14988920. S2CID 28746142.