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Multiple contour extraction from graylevel images using an artificial neural network

Venkatesh, YV and Raja, SK and Ramya, N (2006) Multiple contour extraction from graylevel images using an artificial neural network. In: IEEE Transactions on Image Processing, 15 (4). pp. 892-899.

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For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Item Type: Journal Article
Publication: IEEE Transactions on Image Processing
Publisher: Institute of Electrical and Electronics Engineers
Additional Information: The copyright of this article belongs to the Authors.
Keywords: active contour models (ACMs); edge detection; contour extraction; snakes; self-organizing map (SOM); time-adaptive self-organizing map (TASOM).
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 25 Sep 2010 07:21
Last Modified: 31 May 2022 07:20
URI: https://eprints.iisc.ac.in/id/eprint/31711

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