Detection of ridges and ravines using fuzzy logic operations

Authors:
Kyoung Min Kim;Joong Jo Park;Myung Hyun Song;In Cheol Kim;Ching Y. Suen
Affiliations:
Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia Univ., Montreal, Canada and Dept. of Elec. Eng., Yosu Natl. Univ., San 96-1, Dunduk-dong, Chonnam, Yeosu 550-749, Sout ...;Department of Control and Instrumentation Engineering, Gyeongsang National University, 900, Gazwa-dong, Gyeongnam, Chinju 660-701, South Korea;Department of Electric Control Engineering, Sunchon National University, 315, Maegok-dong, Chonnam, Suncheon 540-742, South Korea;Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, 1455 de Maisonneuve Blvd., West, Suite GM606, Montreal, Canada H3G 1 M8;Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, 1455 de Maisonneuve Blvd., West, Suite GM606, Montreal, Canada H3G 1 M8
Venue:
Pattern Recognition Letters
Year:
2004

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Abstract

In object analysis, line and curve finding plays a universal role, and it can be accomplished by detecting ridges and ravines in digital gray scale images. In this paper, we present a new method of detecting ridges and ravines by using local min and max operations. This method uses erosion and dilation properties of fuzzy logic operations and requires no information of ridge or ravine direction, so that the method is simple and easy in comparison with the conventional analytical methods. Experimental results from different types of images show that the proposed technique is both effective and efficient.