[Bea1989c]
Segmentation Hiérarchique de l’Image par Optimisation Séquentielle
Author:Beaulieu Jean-Marie
In Book:Télédétection et Gestion des Ressources
Bernier, Bonn, Gagnon, Eds., Ste-Foy, Québec
1989, vol. Vol. VI, pp. 245-251
Publisher:Association Québécoise de Télédétection, Québec
Abstract: A hierarchical segmentation algorithm based upon step-wise optimization is presented. The algorithm starts with an initial picture partition, and at each iteration, the two most similar segments are merged by optimizing a “step-wise criterion”. This yields a hierarchical decomposition of the picture, which is data driven with no restriction on segment shapes. The algorithm is designed so as to reduce the computing time. Hence, the fact that a segment merge affects only the surrounding segments is exploited. Good results are produced because of the global and gradual manner of the sequential optimization.
“Segmentation Hiérarchique de l’Image par Optimisation Séquentielle,”
Beaulieu Jean-Marie,
in Télédétection et Gestion des Ressources, Bernier, Bonn, Gagnon, Eds., Ste-Foy, Québec, Association Québécoise de Télédétection, Québec, 1989, vol. Vol. VI, pp. 245-251.
[Bibtex]
@incollection{Bea1989c,
author = {Beaulieu, Jean-Marie},
title = {Segmentation Hi{\'e}rarchique de l'Image par Optimisation S{\'e}quentielle},
booktitle = {T{\'e}l{\'e}d{\'e}tection et Gestion des Ressources},
volume = {Vol. VI},
editor = {Bernier and Bonn and Gagnon},
publisher = {Association Qu{\'e}b{\'e}coise de T{\'e}l{\'e}d{\'e}tection, Qu{\'e}bec},
address = {Ste-Foy, Qu{\'e}bec},
pages = {245-251},
year = {1989},
month = {},
url = {},
isbn = {},
doi = {},
mypdf = {9},
abstract = {A hierarchical segmentation algorithm based upon step-wise optimization is presented. The algorithm starts with an initial picture partition, and at each iteration, the two most similar segments are merged by optimizing a "step-wise criterion". This yields a hierarchical decomposition of the picture, which is data driven with no restriction on segment shapes. The algorithm is designed so as to reduce the computing
time. Hence, the fact that a segment merge affects only the surrounding segments is exploited. Good results are produced because of the global and gradual manner of the sequential optimization.},
keywords = {}
}
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