Conditional cyclic Markov random fields

Kent, John T.; Mardia, Kanti V.; Walder, A. N.

doi:10.1017/s0001867800027257

Cited by 6 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that in general a pth-order MRF model for the edge transformation vector corresponds to a ðp þ 1Þth-order MRF model for the vertex transformation vector. A similar result can be found in Kent et al (1996).…”

Section: Propositionsupporting

confidence: 87%

On the Relation between Edge and Vertex Modelling in Shape Analysis

Hobolth

Kent

Dryden

2002

Scandinavian J Statistics

Self Cite

View full text Add to dashboard Cite

ABSTRACT. Objects in the plane with no obvious landmarks can be described by either vertex transformation vectors or edge transformation vectors. In this paper we provide the relation between the two transformation vectors. Grenander & Miller (1994) use a multivariate normal distribution with a block circulant covariance matrix to model the edge transformation vector. This type of model is also feasible for the vertex transformation vector and in certain cases the free parameters of the two models match up in a simple way. A vertex model and an edge model are applied to a data set of sand particles to explore shape variability.

show abstract

Section: Propositionsupporting

confidence: 87%

On the Relation between Edge and Vertex Modelling in Shape Analysis

Hobolth

Kent

Dryden

2002

Scandinavian J Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Of course this is an approximation, since there are generally continuous edges in the background as well as on the boundary of the target object. There have been some attempts to allow explicitly for this type of dependence | for example, the Markov discriminant of (MacCormick and Blake, 1998b), or MRFs in general (Chellappa and Jain, 1993Kent et al, 1996Winkler, 1995. However, these are too computationally expensive for tracking tasks, so instead we adopt the assumption of independence between measurement lines.…”

Section: Independence Of Measurement Linesmentioning

confidence: 99%

A probabilistic exclusion principle for tracking multiple objects

MacCormick

Blake

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

341

292

View full text Add to dashboard Cite

Tracking multiple targets is a challenging problem, especially when the targets are \identical", in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-tting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled t r ackers, while retaining the desirable properties of coupling.

show abstract

“…( 1)where all indices are modulo the number of vertices n. The parameterisation in (1) is proposed by Kent, Mardia & Walder (1996), and a 0 and a 1 are interpreted as var(t (0) i j rest of t (0) ) = 1=C i;i and corr(t…”

Section: The High-level Image Model 1 a Deformable Template Model Formentioning

confidence: 99%

Bayesian object identification

Rue¹

1999

Biometrika

View full text Add to dashboard Cite

This paper addresses the task of locating and identifying an unknown number of objects of different types in an image. Baddeley & Van Lieshout (1993) advocate marked point processes as object priors, whereas Grenander & Miller (1994) use deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types, using reversible jump Markov chain Monte Carlo methods for inference (Green, 1995). The naive application of these methods here leads to slow mixing and we adapt the model and algorithm in tandem in proposing three strategies to deal with this. The first two expand the model space by introducing an additional 'unknown' object type and the idea of a variable resolution template. The third strategy, utilising the first two, augments the algorithm with classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects.

show abstract

Conditional cyclic Markov random fields

Cited by 6 publications

References 1 publication

On the Relation between Edge and Vertex Modelling in Shape Analysis

On the Relation between Edge and Vertex Modelling in Shape Analysis

A probabilistic exclusion principle for tracking multiple objects

Bayesian object identification

Contact Info

Product

Resources

About