Content deleted Content added
copyedit |
Ectech2000 (talk | contribs) mNo edit summary |
||
Line 7:
In image processing, '''Line detection''' is an algorithm that takes a collection of n [[edge detection|edge points]] and finds all the lines on which these edge points lie.<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=Umbaugh|first=Scott E.|date=2011|publisher=CRC Press|isbn=9781439802052|edition=2nd|___location=Boca Raton, FL|oclc=491888664}}</ref> The most popular line detectors are the [[Hough transform]] and [[Kernel (image processing)|convolution]] based techniques.<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>
The '''[http://www.mathworks.com/help/images/ref/hough.html Hough transform]'''<ref name="CalTech">{{Cite web|url=http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/HoughTrans_lines_09.pdf|title=Line Detection by Hough transformation|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>{{cite web|url=http://vision.stanford.edu/teaching/cs231a_autumn1112/lecture/lecture4_edges_lines_cs231a_marked.pdf |title=Finding lines: from detection to model fitting |first=Fei‐Fei |last=Li |date=10 October 2011 |publisher=Stanford Vision Lab}}</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.
In a [[Kernel (image processing)|'''convolution''']] based technique, the line detector operator consists of a convolution masks tuned to detect the presence of lines of a particular width n and a θ orientation. Here are the four convolution masks to detect horizontal, vertical, oblique (+45 degrees), and oblique (-45 degrees) lines in an image.
<references group="https://homepages.inf.ed.ac.uk/rbf/HIPR2/linedet.htm" />a) Horizontal mask(R1)
| |||