Editing Edge detection (section)

== Motivations ==
[[File:Ääretuvastuse näide.png|thumb|500px|[[Canny edge detection]] applied to a photograph]]
The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world.
It can be shown that under rather general assumptions for an image formation model, discontinuities in image brightness are likely to correspond to:<ref>H.G. Barrow and J.M. Tenenbaum (1981) "Interpreting line drawings as three-dimensional surfaces", Artificial Intelligence, vol 17, issues 1–3, pages 75–116.</ref><ref name=lin95>{{SpringerEOM| title=Edge detection | id=Edge_detection | oldid=17883 | first=Tony | last=Lindeberg }}</ref>
* discontinuities in depth,
* discontinuities in surface orientation,
* changes in material properties and
* variations in scene illumination.
In the ideal case, the result of applying an edge detector to an image may lead to a set of connected curves that indicate the boundaries of objects, the boundaries of surface markings as well as curves that correspond to discontinuities in surface orientation.
Thus, applying an edge detection algorithm to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image.
If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be substantially simplified.
However, it is not always possible to obtain such ideal edges from real life images of moderate complexity.

Edges extracted from non-trivial images are often hampered by ''fragmentation'', meaning that the edge curves are not connected, missing edge segments as well as ''false edges'' not corresponding to interesting phenomena in the image – thus complicating the subsequent task of interpreting the image data.<ref name=lin98>[http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A452310&dswid=386 T. Lindeberg (1998) "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, 30, 2, pages 117–154.]</ref>

Edge detection is one of the fundamental steps in image processing, image analysis, image pattern recognition, and computer vision techniques.