34324524
Feature tracking by multi-frame relaxation
Abstract This paper describes a novel feature tracking method. It is based on an interframe relaxation technique. This method combines intra- and inter-frame constraints on the behaviour of acceptable contour structure. The intra-frame information is represented by both a dictionary of local contour structure and a statistical model of the response of a set of directional feature detection operators. The inter-frame ingredient represents the novel modelling component; it is encapsulated by an implicit model of the underlying surface structure of 3D feature points. The model is represented in terms of a series of unimodal probability densities whose single parameter is the inter-frame distance. The initial probabilities in our relaxation scheme effectively combine distributions describing the statistical uncertainties in the position and feature characteristics of multiframe contours; these probabilities are refined in the light of the dictionary to produce consistent contours. We present an experimental evaluation of the resulting feature detection method on cranial MRI data. Here the method significantly outperforms its single frame counter-part in terms of its ability to extract noise-free and smooth feature contours.
6526675
Density propagation for surface tracking
This paper describes a novel approach to surface tracking in volumetric image stacks. It draws on a statistical model of the uncertainties inherent in the characterisation of feature contours to compute an evidential field for putative inter-frame displacements. This field is computed using Gaussian density kernels which are parameterised in terms of the variance-covariance matrices for contour displacement. The underlying variance model accommodates the effects of raw image noise on the estimated surface normals. The evidential field effectively couples contour displacements to the intensity features on successive frames through a statistical process of contour tracking. Hard contours are extracted using a dictionary-based relaxation process. The method is evaluated on both MRI data and simulated data.
26753616
Inexact Graph Matching with Genetic Search
This paper describes a novel framework for performing relational graph matching using genetic search. The fitness measure is Bayesian in origin. It gauges relational consistency at both the symbolic and attribute levels. The basic measure of symbolic consistency is Hamming distance, while attribute consistency is measured using Mahalanobis distance. We provide examples of the performance on synthetic graphs containing significant levels of clutter. We also demonstrate that the technique is capable of resolving multiple graphs with significant overlap. The performance advantages over deterministic hill climbing are also demonstrated.
16931990
Least-commitment graph matching with genetic algorithms
This paper concerns the correspondence matching of ambiguous feature sets extracted from images. The "rst contribution made in this paper is to extend Wilson and Hancock’s Bayesian matching framework (Wilson and Hancock, IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997) 634}648) by considering the case where the feature measurements are ambiguous. The second contribution is the development of a multimodal evolutionary optimisation framework which is capable of simultaneously producing several good alternative solutions. Previous multimodal genetic algorithms have required additional parameters to be added to a method which is already over-parameterised. The algorithm presented in this paper requires no extra parameters: solution yields are maximised by removing bias in the selection step, while optimisation performance is maintained by a local search step. This framework is in principle applicable to any multimodal optimisation problem where local search performs well. An experimental study demonstrates the e!ectiveness of the new approach on synthetic and real data. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
3249306
Genetic algorithms for ambiguous labelling problems
Abstract Consistent labelling problems frequently have more than one solution. Most work in the field has aimed at disambiguating early in the interpretation process, using only local evidence. This paper starts with a review of the literature on labelling problems and ambiguity. Based on this review, we propose a strategy for simultaneously extracting multiple related solutions to the consistent labelling problem. In a preliminary experimental study, we show that an appropriately modified genetic algorithm is a robust tool for finding multiple solutions to the consistent labelling problem. These solutions are related by common labellings of the most strongly constrained junctions. We have proposed three run-time measures of algorithm performance: the maximum fitness of the genetic algorithm's population, its Shannon entropy, and the total Hamming distance between its distinct members. The results to date indicate that when the Shannon entropy falls below a certain threshold, new solutions are unlikely to emerge and that most of the diversity in the population disappears within the first few generations.
8887249
Inexact graph matching using genetic search
This paper describes a framework for performing relational graph matching using genetic search. There are three novel ingredients to the work. Firstly, we cast the optimisation process into a Bayesian framework by exploiting the recently reported global consistency measure of Wilson and Hancock as a fitness measure. The second novel idea is to realise the crossover process at the level of subgraphs, rather than employing string-based or random crossover. Finally, we accelerate convergence by employing a deterministic hill-climbing process prior to selection. Since we adopt the Bayesian consistency measure as a fitness function, the basic measure of relational distance underpinning the technique is Hamming distance. Our standpoint is that genetic search provides a more attractive means of performing stochastic discrete optimisation on the global consistency measure than alternatives such as simulated annealing. Moreover, the action of the optimisation process is easily understood in terms of its action in the Hamming distance domain. We demonstrate empirically not only that the method possesses polynomial convergence time but also that the convergence rate is more rapid than simulated annealing. We provide some experimental evaluation of the method in the matching of aerial stereograms and evaluate its sensitivity on synthetically generated graphs.
37648606
Genetic Algorithms for Ambiguous Labelling Problems
Consistent labelling problems frequently have more than one solution. Most work in the field has aimed at disambiguating early in the interpretation process, using only local evidence. This paper starts with a review of the literature on labelling problems and ambiguity. Based on this review, we propose a strategy for simultaneously extracting multiple related solutions to the consistent labeling problem. In a preliminary experimental study, we show that an appropriately modified genetic algorithm is a robust tool for finding multiple solutions to the consistent labelling problem. These solutions are related by common labellings of the most strongly constrained junctions. We have proposed three runtime measures of algorithm performance: the maximum fitness of the genetic algorithm's population, its Shannon entropy, and the total Hamming distance between its distinct members. The results to date indicate that when the Shannon entropy falls below a certain threshold, new solutions are unlikely to emerge and that most of the diversity in the population disappears within the first few generations.