Discontinuous MRF prior and robust statistics: a comparative study

doi:10.1016/0262-8856(95)90842-V

Image and Vision Computing

Volume 13, Issue 3, April 1995, Pages 227-233

https://doi.org/10.1016/0262-8856(95)90842-V Get rights and content

Abstract

Discontinuity adaptive MRF priors have been used for modelling vision problems involving discontinuities,- and robust statistics models for solving regression problems involving outliers. This paper presents a comparative study of the two kinds of models. We analyse the mechanisms of adaptation (to discontinuities) and robustness (to outliers), and give a necessary condition for the adaptation and robustness. We then give a common definition of both models. The definition captures the essence of the adaptation ability, and in theory gives infinitely many choices of functions suitable for the adaptation in MRF and robust models. The likeness between the two models suggests that results in the two areas are interchangeable, to the benefit of each other.

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Discontinuous MRF prior and robust statistics: a comparative study

Abstract

Cognitive Sci.

Spatial interaction and the statistical analysis of lattice systems

J. Roy. Statist. Soc. Ser. B

Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images

IEEE Trans., PAMI

Computational vision and regularization theory

Nature

Probabilistic solution of inverse problems

The role of constraints and discontinuities in visible surface reconstruction

Discontinuity detection for visual surface reconstruction

Comput. Vision, Graph. & Image Process.

Analog ‘neuronal’ networks in early vision

Reconstruction without discontinuities

Towards 3D Vision from Range Images: An Optimisation Framework and Parallel Distributed Networks