Active Canny: edge detection and recovery with open active contour models. Issue 12 (30th October 2017)

• Record Type: Journal Article
• Authors: Baştan, Muhammet; Bukhari, Syed Saqib; Breuel, Thomas
• Is Part Of: IET image processing. Volume 11:Issue 12(2017)
• Page Start: 1325
• Page End: 1332
• Publication Date: 2017-10-30
• Subjects: edge detection -- image restoration -- image representation; active canny -- edge detection -- open active contour model -- recovery framework -- local continuity -- smoothness cues -- missing edge recovery -- local edge structures -- edge pixels -- gradient magnitudes -- binary edges -- gradient vector flow -- output snakelets -- image edge representation -- high‐level analysis -- contour segmentation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0336
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.