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Beta-Measure for Probabilistic Segmentation

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Advances in Artificial Intelligence (MICAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6437))

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Abstract

We propose a new model for probabilistic image segmentation with spatial coherence through a Markov Random Field prior. Our model is based on a generalized information measure between discrete probability distribution (β-Measure). This model generalizes the quadratic Markov measure field models (QMMF). In our proposal, the entropy control is achieved trough the likelihood energy. This entropy control mechanism makes appropriate our method for being used in tasks that require of the simultaneous estimation of the segmentation and the model parameters.

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Author information

Authors and Affiliations

  1. Centro de Investigación en Matemáticas, A.C. Jalisco S/N, Colonia Valenciana, C.P. 36240, Guanajuato, Gto., México

    Oscar Dalmau & Mariano Rivera

Authors
  1. Oscar Dalmau
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  2. Mariano Rivera
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Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan Dios Batiz, s/n, Zacatenco, 07738, Mexico City, México

    Grigori Sidorov

  2. Area de Computación, Centro de Investigación en Matemáticas (CIMAT), Callejón de Jalisco s/n, Mineral de Valenciana, 36240, Guanajuato, México

    Arturo Hernández Aguirre

  3. Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE), Ciencias Computacionales, Luis Enrique Erro No. 1, 72840, Santa María Tonantzintla, Puebla,, México

    Carlos Alberto Reyes García

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Dalmau, O., Rivera, M. (2010). Beta-Measure for Probabilistic Segmentation. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Artificial Intelligence. MICAI 2010. Lecture Notes in Computer Science(), vol 6437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16761-4_28

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  • DOI: https://doi.org/10.1007/978-3-642-16761-4_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16760-7

  • Online ISBN: 978-3-642-16761-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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