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Proceedings Paper

K-max: segmentation based on selection of max-tree deep nodes
Author(s): Alexandre G. Silva; Siovani C. Felipussi; Roberto de Alencar Lotufo; Gustavo L. F. Cassol
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Paper Abstract

This work proposes the segmentation of grayscale image from of its hierarchical region based representation. The Maxtree structure has demonstrated to be useful for this purpose, offering a semantic vision of the image, therefore, reducing the number of elements to process in relation to the pixel based representation. In this way, a particular searching in this tree can be used to determine regions of interest with lesser computational effort. A generic application of detection of peaks is proposed through searching nodes to kup steps from leaves in the Max-tree (this operator will be called k-max), being each node corresponds to a connected component. The results are compared with the optimal thresholding and the H-maxima technique.

Paper Details

Date Published: 16 February 2006
PDF: 8 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640M (16 February 2006); doi: 10.1117/12.643462
Show Author Affiliations
Alexandre G. Silva, Univ. of Santa Catarina (Brazil)
Univ. of Campinas (Brazil)
Siovani C. Felipussi, Univ. of Santa Catarina (Brazil)
Roberto de Alencar Lotufo, Univ. of Campinas (Brazil)
Gustavo L. F. Cassol, Univ. of Santa Catarina (Brazil)

Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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