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Type: Artigo de periódico
Title: Multiscale skeletons by image foresting transform and its application to neuromorphometry
Author: Falcao, AX
Costa, LD
da Cunha, BS
Abstract: The image foresting transform (IFT) reduces optimal image partition problems based on seed pixels to a shortest-path forest problem in a graph, whose solution can be obtained in linear time. Such a strategy has allowed a unified and efficient approach to the design of image processing operators, such as edge tracking, region growing, watershed transforms, distance transforms, and connected filters. This paper presents a fast and simple method based on the IFT to compute multiscale skeletons and shape reconstructions without border shifting. The method also generates one-pixel-wide connected skeletons and the skeleton by influence zones, simultaneously, for objects of arbitrary topologies. The results of the work are illustrated with respect to skeleton quality, execution time, and its application to neuromorphometry. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Subject: multiscale skeletons
shape filtering
image analysis
linage foresting transform
Euclidean distance transform
exact dilations
label propagation
graph algorithms
Country: Inglaterra
Editor: Pergamon-elsevier Science Ltd
Citation: Pattern Recognition. Pergamon-elsevier Science Ltd, v. 35, n. 7, n. 1571, n. 1582, 2002.
Rights: fechado
Identifier DOI: 10.1016/S0031-3203(01)00148-0
Date Issue: 2002
Appears in Collections:Unicamp - Artigos e Outros Documentos

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