Hierarchical statistical models for the fusion of multiresolution image data

Jean-Marc Laferte; Fabrice Heitz; Patrick Perez; Eric Fabre

doi:10.1117/12.216365

11 August 1995 Hierarchical statistical models for the fusion of multiresolution image data

Jean-Marc Laferte, Fabrice Heitz, Patrick Perez, Eric Fabre

Proceedings Volume 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing; (1995) https://doi.org/10.1117/12.216365
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

Jean-Marc Laferte, Fabrice Heitz, Patrick Perez, and Eric Fabre "Hierarchical statistical models for the fusion of multiresolution image data", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); https://doi.org/10.1117/12.216365

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