ISSN:0266-4763    

DOI:10.1080/02664769300000055

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

Markov random field models and Bayesian methods have provided answers to various contemporary problems in image analysis. We give a very brief introduction to the topic. In particular, we highlight the use of Bayesian methods in classifying the image into different classes. Some other current developments are also described and their relationship with other chapters in this volume is indicated. Some future directions are also outlined.

ISSN:0266-4763    

DOI:10.1080/02664769300000056

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

There is an extensive literature on image models; perhaps the greatest emphasis has been on models based on Markov random fields. This article briefly discusses some general aspects of image modeling:what should be modeled (the scene rather than the image; both the ‘objects' and the ‘clutter’); the importance of non-standard types of models; the oversimplification of conventional models; possible limitations on the effectiveness of realistic models; models in higher-dimensional domains; and general guidelines for selecting problems to which models can be effectively applied.

ISSN:0266-4763    

DOI:10.1080/02664769300000057

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, non-linear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low-energy states (‘annealing’), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ‘relaxation’ algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.

ISSN:0266-4763    

DOI:10.1080/02664769300000058

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

A continuous two-dimensional region is partitioned into a fine rectangular array of sites, or ‘pixels', each pixel having a particular '‘colour’ belonging to a prescribed finite set. The true colouring of the region is unknown but, associated with each pixel, there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model. The aim is to reconstruct the true scene, with the additional knowledge that pixels close together tend to have the same or similar colours. In this paper, it is assumed that the local characteristics of the true scene can be represented by a non-degenerate Markov random field. Such information can be combined with the records by Bayes' theorem and the true scene can be estimated according to standard criteria. However, the computational burden is enormous and the reconstruction may reflect undesirable large-scale properties of the random field. Thus, a simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics. The method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field. Some complications, including parameter estimation, are discussed. Potential applications are mentioned briefly.

ISSN:0266-4763    

DOI:10.1080/02664769300000059

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

In this paper, we discuss an approach to the pattern synthesis and analysis of biological shapes which show a lot of variability, but at the same time also show a characteristic structure. This structure is captured by way of ‘shape classes' which are constructed via a deformable template. Examples of pattern analysis in both two and three dimensions are presented. We define what we call intrinsic and extrinsic understanding of images and apply this to the detection of abnormalities.

ISSN:0266-4763    

DOI:10.1080/02664769300000060

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

Many of the tasks encountered in image processing can be considered as problems in statistical inference. In particular, they fit naturally into a subjectivist Bayesis framework. In this paper, we describe the Bayesian approach to image analysis. Numerical examples are not included but references are given. It is argued that the Bayesian approach, still in its infancy, has considerable potential for future development.

ISSN:0266-4763    

DOI:10.1080/02664769300000061

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image-processing community.

ISSN:0266-4763    

DOI:10.1080/02664769300000062

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

We describe standard single-site Monte Carlo Markov chain methods, the Hastings and Metropolis algorithms, the Gibbs sampler and simulated annealing, for maximum a posteriori and marginal posterior modes image estimation. These methods can experience great difficulty in traversing the whole image space in a finite time when the target distribution is multi-modal. We present a survey of multiple-site update methods, including Swendsen and Wang's algorithm, coupled Markov chains and cascade algorithms designed to tackle the problem of moving between modes of the posterior image distribution. We compare the performance of some of these algorithms for sampling from degraded and non-degraded Ising models

ISSN:0266-4763    

DOI:10.1080/02664769300000063

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor

摘要:

This paper addresses the image modeling problem under the assumption that images can be represented by third-order, hidden Markov mesh random field models. The range of applications of the techniques described hereafter comprises the restoration of binary images, the modeling and compression of image data, as well as the segmentation of gray-level or multi-spectral images, and image sequences under the short-range motion hypothesis. We outline coherent approaches to both the problems of image modeling (pixel labeling) and estimation of model parameters (learning). We derive a real-time labeling algorithm-based on a maximum, marginal a posteriori probability criterion-for a hidden third-order Markov mesh random field model. Our algorithm achieves minimum time and space complexities simultaneously, and we describe what we believe to be the most appropriate data structures to implement it. Critical aspects of the computer simulation of a real-time implementation are discussed, down to the computer code level. We develop an (unsupervised) learning technique by which the model parameters can be estimated without ground truth information. We lay bare the conditions under which our approach can be made time-adaptive in order to be able to cope with short-range motion in dynamic image sequences. We present extensive experimental results for both static and dynamic images from a wide variety of sources. They comprise standard, infra-red and aerial images, as well as a sequence of ultrasound images of a fetus and a series of frames from a motion picture sequence. These experiments demonstrate that the method is subjectively relevant to the problems of image restoration, segmentation and modeling.

ISSN:0266-4763    

DOI:10.1080/02664769300000064

出版商:Carfax Publishing Company    

年代:1993

数据来源: Taylor