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In [[computer vision]] and [[image processing]] the concept of '''feature detection''' refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an [[image feature]] of a given type at that point or not. The resulting features will be subsets of the image ___domain, often in the form of isolated points, continuous curves or connected regions.
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|+ Common feature detectors and their classification:
!Feature detector!![[Edge detection|
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| Canny
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== References ==
*Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986. (Canny edge detection)
* {{cite conference
| author=C. Harris and M. Stephens
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}}(Grey-level blob detection and scale-space blobs)
*R. Haralick, "Ridges and Valleys on Digital Images," Computer Vision, Graphics, and Image Processing vol. 22, no. 10, pp. 28-38, Apr. 1983. (Ridge detection using facet model)
*J. L. Crowley and A. C. Parker, "A Representation for Shape Based on Peaks and Ridges in the Difference of Low Pass Transform", IEEE Transactions on PAMI, PAMI 6 (2), pp 156-170, March 1984. (Ridge detection based on DOGs)
*D. Eberly , R. Gardner , B. Morse , S. Pizer , C. Scharlach, Ridges for image analysis, Journal of Mathematical Imaging and Vision, v.4 n.4, p.353-373, Dec. 1994. (Fixed scale ridge detection)
*T. Lindeberg: "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, vol 30, number 2, pp. 117--154, 1998. Earlier version presented at IEEE Conference on Pattern Recognition and Computer Vision, CVPR'96, San Francisco, California, pages 465--470,
== See also ==
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