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Dual Active Contour Models for Image Feature Extraction

Record type: Thesis (Doctoral)

Active contours are now a very popular technique for shape extraction, achieved by minimising a suitably formulated energy functional. Conventional active contour formulations suffer difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems, but can compromise other performance aspects. To relieve the problem in initialisation, an evolutionary dual active contour has been developed, which is combined with a local shape model to improve the parameterisation. One contour expands from inside the target feature, the other contracts from the outside. The two contours are inter-linked to provide a balanced technique with an ability to reject weak’local energy minima. Additionally a dual active contour configuration using dynamic programming has been developed to locate a global energy minimum and complements recent approaches via simulated annealing and genetic algorithms. These differ from conventional evolutionary approaches, where energy minimisation may not converge to extract the target shape, in contrast with the guaranteed convergence of a global approach. The new techniques are demonstrated to extract successfully target shapes in synthetic and real images, with superior performance to previous approaches. The new technique employing dynamic programming is deployed to extract the inner face boundary, along with a conventional normal-driven contour to extract the outer face boundary. Application to a database of 75 subjects showed that the outer contour was extracted successfully for 96% of the subjects and the inner contour was successful for 82%. This application highlights the advantages new dual active contour approaches for automatic shape extraction can confer.

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Citation

Gunn, S.R. and Nixon, M.S. (1996) Dual Active Contour Models for Image Feature Extraction University of Southampton, Electronics and Computer Science : University of Southampton, Doctoral Thesis .

More information

Published date: 1996
Additional Information: Address: Faculty of Engineering and Applied Science
Organisations: University of Southampton, Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 250089
URI: http://eprints.soton.ac.uk/id/eprint/250089
PURE UUID: c32cf74e-86c3-40de-8195-e5cc3cc4aaf3

Catalogue record

Date deposited: 28 Oct 2001
Last modified: 18 Jul 2017 10:44

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