Editing Scale invariance (section)

===Computer vision===
{{Main article|Scale space}}
In [[computer vision]] and [[biological vision]], scaling transformations arise because of the perspective image mapping and because of objects having different physical size in the world. In these areas, scale invariance refers to local image descriptors or visual representations of the image data that remain invariant when the local scale in the image domain is changed.<ref name=Lin13PONE>[https://dx.doi.org/10.1371/journal.pone.0066990 Lindeberg, T. (2013) Invariance of visual operations at the level of receptive fields, PLoS ONE 8(7):e66990.]</ref>  
Detecting local maxima over scales of normalized derivative responses provides a general framework for obtaining scale invariance from image data.<ref name=Lindeberg1998>{{cite journal
 | author = Lindeberg, Tony
 | year = 1998
 | title = Feature detection with automatic scale selection
 | journal = International Journal of Computer Vision
 | volume = 30
 | issue = 2
 | pages = 79–116
 | doi = 10.1023/A:1008045108935
 | s2cid = 723210
 | url = https://kth.diva-portal.org/smash/get/diva2:453064/FULLTEXT01
| url-access = 
 | url-status = 
 | archive-url = 
 | archive-date = 
 }}</ref><ref name=Lin14CompVis>T. Lindeberg (2014) [http://www.csc.kth.se/~tony/abstracts/Lin14-ScSel-CompVisRefGuide.html "Scale selection", Computer Vision: A Reference Guide, (K. Ikeuchi, Editor), Springer, pages 701-713.]</ref>
Examples of applications include [[blob detection]], [[corner detection]], [[ridge detection]], and object recognition via the [[scale-invariant feature transform]].