Hierarchical statistical models for the fusion of multiresolution image data
- 19 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 908-913
- https://doi.org/10.1109/iccv.1995.466839
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. An optimal algorithm (inspired from the Viterbi algorithm) is developed to compute the bayesian estimates on the hierarchical graph structures. Estimates are obtained within two passes on the graph structure. This algorithm is non-iterative and yields a per pixel computational complexity which is independent of image size. This approach is compared to the multiscale algorithm proposed by (Bouman et al., 1994) for single-resolution image segmentation (that we have extended for multiresolution data fusion).Keywords
This publication has 9 references indexed in Scilit:
- Global non-linear multigrid optimization for image analysis tasksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A multiscale random field model for Bayesian image segmentationIEEE Transactions on Image Processing, 1994
- Multiscale Minimization of Global Energy Functions in Some Visual Recovery ProblemsCVGIP: Image Understanding, 1994
- Multiscale representations of Markov random fieldsIEEE Transactions on Signal Processing, 1993
- Subsampling of Markov random fieldsJournal of Visual Communication and Image Representation, 1992
- Bayesian estimation of motion vector fieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Hierarchical segmentation using compound Gauss-Markov random fieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Stochastic stereo matching over scaleInternational Journal of Computer Vision, 1989
- A renormalization group approach to image processing problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989