Results 1  10
of
19
Modelling and interpretation of architecture from several images
"... The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a ..."
Abstract

Cited by 111 (6 self)
 Add to MetaCart
(Show Context)
The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a vast amount of specialist time and equipment. Because of this much research has been undertaken in the computer vision community into automating all or part of the process of acquiring a 3D model from a sequence of images. This thesis focuses specifically on the automatic acquisition of architectural models from short image sequences. An architectural model is defined as a set of planes corresponding to walls which contain a variety of labelled primitives such as doors and windows. As well as a label defining its type, each primitive contains parameters defining its shape and texture. The key advantage of this representation is that the model defines not only geometry and texture, but also an interpretation of the scene. This is crucial as it enables reasoning about the scene; for instance, structure and texture can be inferred in areas of the model which are unseen in any
Boundary Detection Through Dynamic Polygons
, 1997
"... A method for the Bayesian restoration of noisy binary images portraying an object with constant greylevel on a background is presented. The restoration, performed by fitting a polygon with any number of sides to the object outline, is driven by a new probabilistic model for the generation of polygon ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
A method for the Bayesian restoration of noisy binary images portraying an object with constant greylevel on a background is presented. The restoration, performed by fitting a polygon with any number of sides to the object outline, is driven by a new probabilistic model for the generation of polygons in a compact subset of R 2 , which is used as a prior distribution for the polygon. The simulation from the prior and the calculation of the a posteriori mean of greylevels are carried out through reversible jump MCMC computation. Two examples of restoration of synthetic images are presented. Keywords and phrases: Bayesian object restoration, probability distribution of polygons, reversible jump MCMC computation. 1 Introduction A difficult task in the restoration of an image portraying objects is the recovery of their boundaries. This can be done at a low level (e.g. using an edge detector, see Rosenfeld and Kak, 1982) or at a high level, trying to embed into the restoration process mo...
Bayesian Object Identification
, 1997
"... This paper addresses the image analysis problem of object recognition  locating and identifying an unknown number of objects of different types in a scene. The particular application in mind is the automatic labelling of cells in a microscope slide. Highlevel statistical image analysis has been th ..."
Abstract

Cited by 19 (1 self)
 Add to MetaCart
This paper addresses the image analysis problem of object recognition  locating and identifying an unknown number of objects of different types in a scene. The particular application in mind is the automatic labelling of cells in a microscope slide. Highlevel statistical image analysis has been the subject of much recent research activity (Baddeley & Van Lieshout, 1993; Grenander & Miller, 1994). The former of these approaches advocates marked point processes as object priors; the latter approach is built around the use of deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types. The complexity of the posterior distribution of interest, together with the variable dimension of the parameter space, mean that reversible jump Markov chain Monte Carlo methods are required (Green, 1995). The naive application of these methods here leads to slow mixing; we propose three strategies to deal with this. This first two expand the model space by introducing an additional "unknown" object type and the idea of a variable resolution template. The third strategy is to include classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects. A novel point estimator for the number of objects together with their locations, shapes and types is suggested and applied to an example of microscopy data. SOME KEY WORDS: Bayesian inference; Deformable templates; Image analysis; Loss functions; Marked point processes; Markov chain Monte Carlo; Object recognition; Variable dimension distributions.
Random dynamics and thermodynamic limits for polygonal Markov fields
 in the plane. Advances in Applied Probability 37
, 2005
"... Abstract: We construct random dynamics on collections of nonintersecting planar contours, leaving invariant the distributions of length and areainteracting polygonal Markov fields with Vshaped nodes. The first of these dynamics is based on the dynamic construction of consistent polygonal fields, ..."
Abstract

Cited by 9 (5 self)
 Add to MetaCart
Abstract: We construct random dynamics on collections of nonintersecting planar contours, leaving invariant the distributions of length and areainteracting polygonal Markov fields with Vshaped nodes. The first of these dynamics is based on the dynamic construction of consistent polygonal fields, as presented in the original articles by Arak (1982) and Arak & Surgailis (1989, 1991), and it provides an easytoimplement Metropolistype simulation algorithm. The second dynamics leads to a graphical construction in the spirit of Fernández, Ferrari & Garcia (1998,2002) and it yields a perfect simulation scheme in a finite window from the infinitevolume limit. This algorithm seems difficult to implement, yet its value lies in that it allows for theoretical analysis of thermodynamic limit behaviour of lengthinteracting polygonal fields. The results thus obtained include the uniqueness and exponential αmixing of the thermodynamic limit of such fields in the low temperature region, in the class of infinitevolume Gibbs measures without infinite contours. Outside this class we conjecture the existence of an infinite number of extreme phases breaking both the translational and rotational symmetries.
Perfect simulation for lengthinteracting polygonal Markov fields in the plane, Scand
 Journal of Statistics
, 2007
"... The purpose of this paper is to construct perfect samplers for lengthinteracting Arak{Cliord{Surgailis polygonal Markov elds in the plane with nodes of order 2 (Vshaped nodes). This is achieved by providing for the polygonal elds a hard core marked point process representation with individual poin ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
(Show Context)
The purpose of this paper is to construct perfect samplers for lengthinteracting Arak{Cliord{Surgailis polygonal Markov elds in the plane with nodes of order 2 (Vshaped nodes). This is achieved by providing for the polygonal elds a hard core marked point process representation with individual points carrying polygonal loops as their marks, so that the coupling from the past and clan of ancestors routines can be adopted.
Information bounds for Gibbs samplers
 In preparation
, 1995
"... If we wish to efficiently estimate the expectation of an arbitrary function on the basis of the output of a Gibbs sampler, which is better: deterministic or random sweep? In each case we calculate the asymptotic variance of the empirical estimator, the average of the function over the output, and de ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
If we wish to efficiently estimate the expectation of an arbitrary function on the basis of the output of a Gibbs sampler, which is better: deterministic or random sweep? In each case we calculate the asymptotic variance of the empirical estimator, the average of the function over the output, and determine the minimal asymptotic variance for estimators that use no information about the underlying distribution. The empirical estimator has noticeably smaller variance for deterministic sweep. The variance bound for random sweep is in general smaller than for deterministic sweep, but the two are equal if the target distribution is continuous. If the components of the target distribution are not strongly dependent, the empirical estimator is close to efficient under deterministic sweep, and its asymptotic variance approximately doubles under random sweep. 1 Introduction The Gibbs sampler is a widely used Markov chain Monte Carlo (MCMC) method for estimating analytically intractable feature...
Object Restoration Through Dynamic Polygons
 J. R. Statist. Soc. B
, 1995
"... this article we will try to recover the boundary of a single object starting from a noisy version of it, relying on the prior information that the boundary is a closed and nonintersecting curve in the plane, that is, fitting a nonintersecting polygon to the object. In doing this, we can use more i ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
this article we will try to recover the boundary of a single object starting from a noisy version of it, relying on the prior information that the boundary is a closed and nonintersecting curve in the plane, that is, fitting a nonintersecting polygon to the object. In doing this, we can use more information than that required by a simple edge detector (such as the gradient of greylevel intensity, whose nature is essentially local), because a polygon, besides this information, conveys the idea of connectivity between the pairs of its vertices (not depending on their distance), and the global idea of closed curve. A similar approach to this problem has been attempted by several authors, including Grenander
Efficient estimation in Markov chain models: an introduction
"... We outline the theory of efficient estimation for semiparametric Markov chain models, and illustrate in a number of simple cases how the theory can be used to determine lower bounds for the asymptotic variance of estimators and to construct efficient estimators. In particular, we consider estimation ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
We outline the theory of efficient estimation for semiparametric Markov chain models, and illustrate in a number of simple cases how the theory can be used to determine lower bounds for the asymptotic variance of estimators and to construct efficient estimators. In particular, we consider estimation of stationary distributions of Markov chains, of autoregression parameters and innovation distributions in AR and ARCHmodels and more general time series, and of parameters in quasilikelihood models. AMS 1991 subject classifications. Primary 62M05; secondary 62F12, 62F35, 62G20, 62M10. Key words and Phrases. Variance bound, empirical estimator, martingale approximation, maximum likelihood estimator, KullbackLeibler information, estimating equation, misspecified model, weighted least squares, conditional heteroscedasticity, quasilikelihood, Markov chain Monte Carlo, Gibbs sampler. 1 Introduction The basic example of a time series is the autoregressive process X i = ffX i\Gamma1 + " ...
DISAGREEMENT GRAPH FOR MULTICOLOURED POLYGONAL MARKOV FIELDS
"... In this paper, we give background information about multicoloured polygonal Markov fields and their dynamic representation. Also we work out the details of the disagreement graph for multicoloured polygonal Markov fields. Index Terms — Arak process, dynamic representation, disagreement loop, disa ..."
Abstract
 Add to MetaCart
(Show Context)
In this paper, we give background information about multicoloured polygonal Markov fields and their dynamic representation. Also we work out the details of the disagreement graph for multicoloured polygonal Markov fields. Index Terms — Arak process, dynamic representation, disagreement loop, disagreement graph, image segmentation, multicoloured polygonal Markov fields. 1.
Robust 3D Plane Fitting with Geometric Constraints
"... This paper describes an algorithm for robustly fitting bounded planes to a set of 3 dimensional (3D) points. The algorithm is formulated as a search for maximum a posteriori (MAP) plane parameters, and makes use of strong prior distributions to constrain the shape and orientation of each plane. It d ..."
Abstract
 Add to MetaCart
(Show Context)
This paper describes an algorithm for robustly fitting bounded planes to a set of 3 dimensional (3D) points. The algorithm is formulated as a search for maximum a posteriori (MAP) plane parameters, and makes use of strong prior distributions to constrain the shape and orientation of each plane. It does not assume that all 3D points lie on one of the planes being fitted. An efficient technique for evaluation of model likelihood is also presented, which allows a rapid search through a large number of planar models. Results are shown using this algorithm as part of a modelbased architecture reconstruction system. 1