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Proceedings Paper

Hierarchical statistical models for the fusion of multiresolution image data
Author(s): Jean-Marc Laferte; Fabrice Heitz; Patrick Perez; Eric Fabre
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Paper Abstract

This paper presents a class of nonlinear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. The approach combines nonlinear causal Markov models defined on hierarchical graph structures, with standard Bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or 'ternary graphs') are introduced to represent the multiresolution observations at hand and the hidden labels to be estimated. Two optimal algorithms (inspired from Viterbi's algorithm) are developed on the quadtree structure. The first one gives the exact solution for the MAP (maximum a posteriori) estimator. The second one gives, on the same structure, the global minimum of an energy function more relevant than the MAP criterion. These algorithms are noniterative, estimates being obtained within two passes on the graph structure. They are compared to an extension of the multiscale algorithm proposed by Bouman et al. which is adapted here to multiresolution data fusion.

Paper Details

Date Published: 11 August 1995
PDF: 12 pages
Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); doi: 10.1117/12.216365
Show Author Affiliations
Jean-Marc Laferte, Institut de Recherche en Informatique et Systemes Aleatoires (France)
Fabrice Heitz, Ecole Nationale Superieure de Physique (France)
Patrick Perez, Institut de Recherche en Informatique et Systemes Aleatoires (France)
Eric Fabre, Institut de Recherche en Informatique et Systemes Aleatoires (France)


Published in SPIE Proceedings Vol. 2568:
Neural, Morphological, and Stochastic Methods in Image and Signal Processing
Edward R. Dougherty; Francoise J. Preteux; Sylvia S. Shen, Editor(s)

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