期刊
PATTERN RECOGNITION
卷 35, 期 7, 页码 1571-1582出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(01)00148-0
关键词
multiscale skeletons; shape filtering; image analysis; linage foresting transform; Euclidean distance transform; exact dilations; label propagation; neuromorphometry; graph algorithms
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.
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