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Probabilistic linear models for multiresolution estimation in gray-level images

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Abstract

A multiresolution analysis of digital gray-level images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.

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Authors and Affiliations

  1. Dept. of Applied Mathematics, University of Granada, 18071, Granada, Spain

    José Martínez-Aroza

  2. Dept. of Applied Physics, University of Granada, 18071, Granada, Spain

    Ramón Román-Roldán

Authors
  1. José Martínez-Aroza
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  2. Ramón Román-Roldán
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This work was partially supported by grant TIC91-646 from the DGYCIT of the Spanish Government.

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Martínez-Aroza, J., Román-Roldán, R. Probabilistic linear models for multiresolution estimation in gray-level images. Multidim Syst Sign Process 6, 7–35 (1995). https://doi.org/10.1007/BF00980143

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  • DOI: https://doi.org/10.1007/BF00980143

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