Open AccessRapid contour detection for image classification
Author(s) -
Rasche Christoph
Publication year - 2018
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.2017.1066
Subject(s) - artificial intelligence , edge detection , maxima and minima , computer science , pattern recognition (psychology) , canny edge detector , computer vision , image (mathematics) , enhanced data rates for gsm evolution , corner detection , mathematics , image processing , mathematical analysis
The author introduces a contour detection method that has relatively low complexity yet still highly accurate. The method is based on extrema detection along the four principal orientations, a trick that can be used to detect not only edges but, in particular, also ridges and rivers. The author makes a comparison to the popular Canny algorithm and shows that the proposed method's only downside is that it cannot detect very high curvatures in edge contours. The method is applied to the task of image classification (satellite images, Caltech‐101, etc.) and it is demonstrated that the use of all three contour types (edges, ridges, and rives) improves classification accuracy as opposed to the use of only edge contours. Thus, for image classification, it is more important to extract multiple contour features; the use of the exact detection method appears to play a smaller role. The author's simple method is also appealing for use in individual frames, due to its low complexity.