Feature detection (computer vision): Difference between revisions

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{{unreferencedUnreferenced|article|date=August 2007}}

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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== Types of image features ==

=== [[Edge_detectionEdge detection|Edges]] ===

=== [[Edge_detection|Edges]] ===

Edges are points where there is a boundary (or an edge) between two image regions. In general, an edge can be of almost arbitrary shape, and may include junctions. In practice, edges are usually defined as sets of points in the image which have a strong [[gradient]] magnitude. Furthermore, some common algorithms will then chain high gradient points together to form a more complete description of an edge. These algorithms may place some constraints on the shape of an edge.

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Locally, edges have a one dimensional structure.

=== [[Corner_detectionCorner detection|Corners / interest points]] ===

The terms corners and interest points are used somewhat interchangeably and refer to point-like features in an image, which have a local two dimensional structure. The name "Corner" arose since early algorithms first performed edge detection, and then analysed the edges to find rapid changes in direction (corners). These algorithms were then developed so that explicit edge detection was no longer required, for instance by looking for high levels of [[curvature]] in the image gradient. It was then noticed that the so-called corners were also being detected on parts of the image which were not corners in the traditional sense (for instance a small bright spot on a dark background may be detected). These points are frequently known as interest points, but the term "corner" is used by tradition.

=== [[Blob_detectionBlob detection|Blobs / regions of interest or interest points]] ===

Blobs provide a complementary description of image structures in terms of regions, as opposed to corners that are more point-like. Nevertheless, blob descriptors often contain a preferred point (a local maximum of an operator response or a center of gravity) which means that many blob detectors may also be regarded as interest point operators. Blob detectors can detect areas in an image which are too smooth to be detected by a corner detector.

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Consider shrinking an image and then performing corner detection. The detector will respond to points which are sharp in the shrunk image, but may be smooth in the original image. It is at this point that the difference between a corner detector and a blob detector becomes somewhat vague. To a large extent, this distinction can be remedied by including an appropriate notion of scale. Nevertheless, due to their response properties to different types of image structures at different scales, the LoG and DoH [[blob detection|blob detectors]] are also mentioned in the article on [[corner detection]].

=== [[Ridge_detectionRidge detection|Ridges]] ===

For elongated objects, the notion of ''ridges'' is a natural tool. A ridge descriptor computed from a grey-level image can be seen as a generalization of the a medial axis. From a practical viewpoint, a ridge can be thought of as a one-dimensional curve that represents an axis of symmetry, and in addition has an attribute of local ridge width associated with each ridge point. Unfortunately, however, it is algorithmically harder to extract ridge features from general classes of grey-level images than edge-, corner- or blob features. Nevertheless, ridge descriptors are frequently used for road extraction in aerial images and for extracting blood vessels in medical images -- see [[ridge detection]].

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{|

|+ Common feature detectors and their classification:

!Feature detector!![[Edge_detectionEdge detection| Edge ]]!![[Corner_detectionCorner detection| Corner ]]!![[Blob_detectionBlob detection| Blob ]]

|-

| Canny

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== See also ==

* [[Edge detection ]]

* [[Corner detection]]

* [[Blob detection ]]

* [[Ridge detection ]]

* [[Interest point detection]]

* [[Feature extraction]]

* [[Feature (Computer vision)]]

* [[Computer vision]]

[[Category:Computer vision]]