NDLI: Automatic graph extraction from color images

Content Provider	IEEE Xplore Digital Library
Author	Lourens, T. Okuno, H.G. Kitano, H.
Copyright Year	2001
Description	Author affiliation: ERATO, Japan Sci. & Technol. Corp., Tokyo, Japan (Lourens, T.)
Abstract	An approach to symbolic contour extraction is described that consists of three stages: enhancement, detection, and extraction of contours and corners. Contours and corners are enhanced by models of monkey cortical complex and endstopped cells. Detection of corners and local contour maxima is performed by selection of local maxima in both contour and corner enhanced images. These maxima form the anchor points of a greedy contour following algorithm that extracts the contours. This algorithm is based on the idea of spatially linking neurons along a contour that fire in synchrony to indicate an extracted contour. The extracted contours and detected corners represent the symbolic representation of the image. The advantage of the proposed model over other models is that the same low constant thresholds for corner and local contour maxima detection are used for different images. Closed contours are guaranteed by the contour following algorithm to yield a fully symbolic representation which is more suitable for reasoning and recognition. In this respect our methodology is unique, and clearly different from the standard (edge) contour detection methods. The results of the extracted contours (when displayed as being detected) show similar or better results compared to the SUSAN and Canny-CSS detectors.
Sponsorship	Int. Assoc. Pattern Recognition (IAPR)
Starting Page	302
Ending Page	308
File Size	952840
Page Count	7
File Format	PDF
ISBN	076951183X
DOI	10.1109/ICIAP.2001.957026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Color Image edge detection Object detection Object recognition Symbiosis Joining processes Neurons Fires Detectors Humans
Content Type	Text
Resource Type	Article

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