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Title: Multiple contour extraction from graylevel images using an artificial neural network
Authors: Venkatesh, Y.V. 
Raja, S.K.
Ramya, N.
Keywords: Active contour models (ACMs)
Contour extraction
Edge detection
Self-organizing map (SOM)
Time-adaptive self-organizing map (TASOM)
Issue Date: Apr-2006
Citation: Venkatesh, Y.V., Raja, S.K., Ramya, N. (2006-04). Multiple contour extraction from graylevel images using an artificial neural network. IEEE Transactions on Image Processing 15 (4) : 892-899. ScholarBank@NUS Repository.
Abstract: 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 an 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. © 2006 IEEE.
Source Title: IEEE Transactions on Image Processing
ISSN: 10577149
DOI: 10.1109/TIP.2005.863934
Appears in Collections:Staff Publications

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