Add: bibcode, authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Abductive | Category:Image processing | #UCB_Category 135/253
Other first-order difference operators for estimating image gradient have been proposed in the [[Prewitt operator]], [[Roberts cross]], Kayyali<ref>{{Cite journal|lastlast1=Dim|firstfirst1=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|pages=1–8|doi=10.1155/2013/584816|issn=1687-9309|doi-access=free}}</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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=== Subpixel ===
To increase the precision of edge detection, several subpixel techniques had been proposed, including curve-fitting, moment-based,<ref>{{Cite journal|lastlast1=Ghosal|firstfirst1=S.|last2=Mehrota|first2=R|date=1993-01-01|title=Orthogonal Moment Operators for Subpixel Edge Detection|journal=Pattern Recognition|volume=26|issue=2|pages=295–306|doi=10.1016/0031-3203(93)90038-X|bibcode=1993PatRe..26..295G }}</ref><ref name="Christian">{{Cite journal|last=Christian|first=John|date=2017-01-01|title=Accurate Planetary Limb Localization for Image-Based Spacecraft Navigation|journal=Journal of Spacecraft and Rockets|volume=54|issue=3|pages=708–730|doi=10.2514/1.A33692|bibcode=2017JSpRo..54..708C}}</ref> reconstructive, and partial area effect methods.<ref>{{Cite journal|lastlast1=Trujillo-Pino|firstfirst1=Agustín|last2=Krissian|first2=Karl|last3=Alemán-Flores|first3=Miguel|last4=Santana-Cedrés|first4=Daniel|date=2013-01-01|title=Accurate subpixel edge ___location based on partial area effect|journal=Image and Vision Computing|volume=31|issue=1|pages=72–90|doi=10.1016/j.imavis.2012.10.005|hdl=10553/43474|hdl-access=free}}</ref> These methods have different characteristics. Curve fitting methods are computationally simple but are easily affected by noise. Moment-based methods use an integral-based approach to reduce the effect of noise, but may require more computations in some cases. Reconstructive methods use horizontal gradients or vertical gradients to build a curve and find the peak of the curve as the sub-pixel edge. Partial area effect methods are based on the hypothesis that each pixel value depends on the area at both sides of the edge inside that pixel, producing accurate individual estimation for every edge pixel. Certain variants of the moment-based technique have been shown to be the most accurate for isolated edges.<ref name="Christian"/>[[File:Subpixel edge detection.png|thumb|754x754px|Edge detection on an [[Angiography|angiographic image]]. On the left, edge detection is made at a pixel level. On the right, subpixel edge detection locates the edge inside the pixel precisely|none]]
