Abstract
We give in the paper a brief overview of how the Gibbs fields and related dynamics approaches are applied in image processing. We discuss classical pixel-wise models as well as more recent spatial point process models in the framework of the Gibbs fields approach. We present a new multi-object adapted algorithm for object detection based on a spatial birth-and-death process and a discrete time approximation of this process.
Highlights
Statistical physics and probabilistic approaches have been brought in image analysis starting with the famous paper by Besag in 1974 [5]
The goal of this paper is to present a brief overview of how the Gibbs field approach is applied in image processing
We have given a brief overview of Gibbs field approaches in image processing
Summary
Statistical physics and probabilistic approaches have been brought in image analysis starting with the famous paper by Besag in 1974 [5]. The basic idea in [16] was to rewrite a restoration procedure in the language of statistical physics using concepts of statistical ensembles, equilibrium and non-equilibrium dynamics From this point of view, a digital image is considered as a configuration (random variables forming a set of random vectors) {X} of a Gibbs field on the lattice with P (X) as joint probability distribution. Using the local interaction property, computations of the transition probabilities become local, i.e. they involve only a finite set of neighboring variables In this connection, a choice of stochastic dynamics which is maximum adapted to a specific problem under consideration is a crucial step in the construction of the algorithm
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