Open AccessActive Canny: edge detection and recovery with open active contour models
Author(s) -
Baştan Muhammet,
Bukhari Syed Saqib,
Breuel Thomas
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0336
Subject(s) - canny edge detector , edge detection , image gradient , active contour model , artificial intelligence , deriche edge detector , enhanced data rates for gsm evolution , vector flow , pixel , computer vision , computer science , smoothness , image segmentation , pattern recognition (psychology) , segmentation , representation (politics) , mathematics , image (mathematics) , image processing , mathematical analysis , politics , political science , law
The authors introduce an edge detection and recovery framework based on open active contour models (snakelets) to mitigate the problem of noisy or broken edges produced by classical edge detection algorithms, like Canny. The idea is to utilise the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking edge pixels based on a threshold. The authors initialise short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges and provide a smooth edge representation of the image; they can also be used for higher‐level analysis, like contour segmentation.