Abstract
This paper defines the basis of a new hierarchical segmentation framework based on an energy minimization scheme. This new framework is based on two formal tools. First, a combinatorial pyramid encodes efficiently a hierarchy of partitions. Secondly, discrete geometric estimators measure precisely some important geometric parameters of the regions. These measures combined with photometrical and topological features of the partition allow to design energy terms based on discrete measures. Our segmentation framework exploits these energies to build a pyramid of image partitions with a minimization scheme. Some experiments illustrating our framework are shown and discussed.
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de Calignon, M.B., Brun, L., Lachaud, JO. (2006). Combinatorial Pyramids and Discrete Geometry for Energy-Minimizing Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_32
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DOI: https://doi.org/10.1007/11919629_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
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