Open main menu
Home
Random
Recent changes
Special pages
Community portal
Preferences
About Wikipedia
Disclaimers
Incubator escapee wiki
Search
User menu
Talk
Dark mode
Contributions
Create account
Log in
Editing
Edge detection
(section)
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
=== Subpixel === To increase the precision of edge detection, several subpixel techniques had been proposed, including curve-fitting, moment-based,<ref>{{Cite journal|last1=Ghosal|first1=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|last1=Trujillo-Pino|first1=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]]
Edit summary
(Briefly describe your changes)
By publishing changes, you agree to the
Terms of Use
, and you irrevocably agree to release your contribution under the
CC BY-SA 4.0 License
and the
GFDL
. You agree that a hyperlink or URL is sufficient attribution under the Creative Commons license.
Cancel
Editing help
(opens in new window)