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In image processing, a line is a collection of edge points that are adjacent and have the same direction.<ref name=":0">{{Cite book|url=https://www.worldcat.org/oclc/491888664|title=Digital image processing and analysis : human and computer vision applications with CVIPtools|last=E.|first=Umbaugh, Scott|date=2011|publisher=CRC Press|others=Umbaugh, Scott E.|isbn=9781439802052|edition=2nd ed|___location=Boca Raton, FL|oclc=491888664}}</ref> Line detection is an algorithm that take a collection of n edge points and find all the lines on which these edge points lie. The most popular line detectors are the '''Hough transform''' and '''convolution''' based technique. <ref>{{Cite web|url=http://www.mathworks.com/help/images/ref/hough.html|title=Hough transform - MATLAB hough|website=www.mathworks.com|access-date=2018-04-23}}</ref>
'''Hough transform'''<ref>{{Cite web|url=http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/HoughTrans_lines_09.pdf|title=|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref> can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ).<ref name=":0" /> If we have a line in our row and column based image space, we can define that line by ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the line measured in degrees clockwise from the positive row axis. [2] Therefore, a line in the image corresponds to a point in the Hough space.[<ref><nowiki>http://vision.stanford.edu/teaching/cs231a_autumn1112/lecture/lecture4_edges_lines_cs231a_marked.pdf</nowiki>
</ref>] The Hough space for lines has therefore these two dimensions θ and ρ, and a line is represented by a single point corresponding to a unique set of these parameters.
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