Feature detection (computer vision): Difference between revisions

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{{FeatureDetectionCompVisNavbox}}

In [[computer vision]] and [[image processing]] the concept of '''feature detection''' refers toincludes methods that aim atfor 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.

== Definition of a feature ==

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== Feature extraction ==

Once features have been detected, a local image patch around the feature can be extracted. This extraction may involve quite considerable amounts of image processing. The result is known as a feature descriptor or feature vector. Among the approaches that are used to feature description, one can mention [[N-jet|''N''-jets]]s and local histograms (see [[scale-invariant feature transform]] for one example of a local histogram descriptor). In addition to such attribute information, the feature detection step by itself may also provide complementary attributes, such as the edge orientation and gradient magnitude in edge detection and the polarity and the strength of the blob in blob detection.

== See also ==

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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, ppp. 353-373353–373, Dec. 1994. (Fixed scale ridge detection)

* {{cite journal

| author=T. Lindeberg