Edge detection: Difference between revisions

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:<math>\theta = \operatorname{atan2}(L_y, L_x).</math>

Other first-order difference operators for estimating image gradient have been proposed in the [[Prewitt operator]], [[Roberts cross]], Kayyali<ref>{{Cite journal|last1=Dim|first1=Jules R.|last2=Takamura|first2=Tamio|date=2013-12-11|title=Alternative Approach for Satellite Cloud Classification: Edge Gradient Application|journal=Advances in Meteorology|language=en|volume=2013|issue=1 |pages=1–8|doi=10.1155/2013/584816|issn=1687-9309|doi-access=free|bibcode=2013AdMet201384816D }}</ref> operator and [[Frei–Chen operator]].

It is possible to extend filters dimension to avoid the issue of recognizing edge in low [[Signal-to-noise ratio|SNR]] image. The cost of this operation is loss in terms of resolution. Examples are Extended Prewitt 7×7.

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=== Connectivity of gradients without using (high) magnitude thresholds ===

This method finds connected set of pixels having a directional derivative magnitude larger than a fairly small threshold.<ref>{{Cite journal |lastlast1=Pak |firstfirst1=Mesut |last2=Bayazit |first2=Ulug |date=2020-07-01 |title=Regional bit allocation with visual attention and distortion sensitivity |url=https://link.springer.com/article/10.1007/s11042-020-08686-z |journal=Multimedia Tools and Applications |language=en |volume=79 |issue=27 |pages=19239–19263 |doi=10.1007/s11042-020-08686-z |issn=1573-7721|url-access=subscription }}</ref> It considers only presence of gradients instead of strength of gradients. After applying a dramatically small threshold (i.e. 5), a binary image is obtained. The morphological opening operation and the morphological closing operation are applied to the binary image to close gaps. Then, the distance transform operation is applied to the binary image to clear the pixels far from the background, so blob-like shapes or other false labeled regions are deleted from the edge map.

=== Edge thinning ===

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[[Marr–Hildreth algorithm|The Marr-Hildreth edge detector]]<ref>{{Cite book |last=Gonzalez |first=Rafael |title=Digital Image Processing |publisher=Pearson Education |year=2018 |isbn=978-0-13-335672-4 |edition=4th}}</ref> is distinguished by its use of the Laplacian of Gaussian (LoG) operator for edge detection in digital images. Unlike other edge detection methods, the LoG approach combines Gaussian smoothing with second derivative operations, allowing for simultaneous noise reduction and edge enhancement. The key advantage of this method lies in its ability to detect edges at various scales by adjusting the standard deviation of the Gaussian kernel, enabling detection of fine details as well as broader transitions. Moreover, the technique leverages zero-crossing detection on the LoG response to precisely locate edges, offering robustness against noise and maintaining edge continuity. This approach is particularly effective for detecting edges with clear boundaries in images while minimizing false positives due to noise, making it a valuable tool in computer vision applications where accurate edge localization is crucial.

== Code for Edgeedge Detectiondetection using Prewitt, Scharr and Sobel Operatoroperator ==

Source:<ref>{{Cite web |date=2021-10-11 |title=Edge detection using Prewitt, Scharr and Sobel Operator |url=https://www.geeksforgeeks.org/edge-detection-using-prewitt-scharr-and-sobel-operator/ |access-date=2024-05-08 |website=GeeksforGeeks |language=en-US}}</ref>

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</syntaxhighlight>

=== Edge Detectiondetection using Sobel operator ===

<syntaxhighlight lang="matlab" line="1">

% MATLAB code for Sobel operator

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==References==

M. Gupta, S.N. Tazi, A. Jain and Deepika, "Edge Detection Using Modified Firefly Algorithm", Computational Intelligence and Communication Networks (CICN) 2014 International onference on, pp. 167-173, 14-16 Nov. 2014.

[https://ieeexplore.ieee.org/document/7065467 View Article]

[https://scholar.google.com/scholar?as_q=Edge+Detection+Using+Modified+Firefly+Algorithm&as_occt=title&hl=en&as_sdt=0%2C31 Google Scholar]

<references />

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*[[:doi:10.5201/ipol.2012.gjmr-lsd|A-contrario line segment detection with code and on-line demonstration]]

* [https://www.mathworks.com/discovery/edge-detection.html Edge detection using MATLAB]

* [http://www.mathworks.com/matlabcentral/fileexchange/48908-accurate-subpixel-edge-___location Subpixel edge detection using Matlab] {{Webarchive|url=https://web.archive.org/web/20211216123504/http://www.mathworks.com/matlabcentral/fileexchange/48908-accurate-subpixel-edge-___location |date=2021-12-16 }}

* [https://photokit.com/tools/effects/edgedetect/ Image Tools Effects - Edgedetect]

* [https://sdk.docutain.com/blogartikel/edge-detection-for-image-processing Edge Detection for Image Processing]