Editing HSL and HSV (section)

==Use in image analysis==
{{see also|Computer vision|Image analysis}}
HSL, HSV, HSI, or related models are often used in [[computer vision]] and [[image analysis]] for [[feature detection (computer vision)|feature detection]] or [[segmentation (image processing)|image segmentation]]. The applications of such tools include object detection, for instance in [[machine vision|robot vision]]; [[object recognition]], for instance of [[facial recognition system|faces]], [[optical character recognition|text]], or [[automatic number plate recognition|license plates]]; [[content-based image retrieval]]; and [[medical imaging|analysis of medical images]].<ref name=Cheng/>

For the most part, computer vision algorithms used on color images are straightforward extensions to algorithms designed for [[grayscale]] images, for instance [[k-means clustering|k-means]] or [[fuzzy clustering]] of pixel colors, or [[canny edge detector|canny]] [[edge detection]]. At the simplest, each color component is separately passed through the same algorithm. It is important, therefore, that the [[feature (computer vision)|features]] of interest can be distinguished in the color dimensions used. Because the ''R'', ''G'', and ''B'' components of an object's color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult. Descriptions in terms of hue/lightness/chroma or hue/lightness/saturation are often more relevant.<ref name=Cheng/>

Starting in the late 1970s, transformations like HSV or HSI were used as a compromise between effectiveness for segmentation and computational complexity. They can be thought of as similar in approach and intent to the neural processing used by human color vision, without agreeing in particulars: if the goal is object detection, roughly separating hue, lightness, and chroma or saturation is effective, but there is no particular reason to strictly mimic human color response. John Kender's 1976 master's thesis proposed the HSI model. Ohta et al. (1980) instead used a model made up of dimensions similar to those we have called {{mvar|I}}, ''α'', and ''β''. In recent years, such models have continued to see wide use, as their performance compares favorably with more complex models, and their computational simplicity remains compelling.{{refn|group=upper-alpha |The Ohta et al. model has parameters {{math|1=''I''<sub>1</sub> = (''R'' + ''G'' + ''B'')/3}}, {{math|1=''I''<sub>2</sub> = (''R'' − ''B'')/2}}, {{math|1=''I''<sub>3</sub> = (2''G'' − ''R'' − ''B'')/4}}. {{math|''I''<sub>1</sub>}} is the same as our {{mvar|I}}, and {{math|''I''<sub>2</sub>}} and {{math|''I''<sub>3</sub>}} are similar to our ''β'' and ''α'', respectively, except that (a) where ''α'' points in the direction of ''R'' in the "chromaticity plane", {{math|''I''<sub>3</sub>}} points in the direction of ''G'', and (b) the parameters have a different linear scaling which avoids the {{radic|3}} of our ''β''.}}<ref name=Cheng/><ref>John Kender (1976). "Saturation, hue and normalized color". Carnegie Mellon University, Computer Science Dept. Pittsburgh, PA.</ref><ref>{{cite journal|author1=Yu-Ichi Ohta |author2=Takeo Kanade |author3=Toshiyuki Sakai |doi=10.1016/0146-664X(80)90047-7|title=Color information for region segmentation|year=1980|journal=Computer Graphics and Image Processing|volume=13|issue=3|pages=222}}</ref><ref>{{cite journal|author1=Ffrank Perez |author2=Christof Koch |doi=10.1007/BF01420983|title=Toward color image segmentation in analog VLSI: Algorithm and hardware|year=1994|journal=International Journal of Computer Vision|volume=12|pages=17–42|s2cid=6140819 |url=https://authors.library.caltech.edu/40488/1/370338.pdf}}</ref>
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Demarty, C.-H., Beucher, S., 1998. Color segmentation algorithm using an HLS transformation. In: Proceedings of the International Symposium on Mathematical Morphology (ISMM ’98). {{nobr|pp. 231–238}}.
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