In:
Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 48, No. 2 ( 1999-06-01), p. 211-227
Abstract:
A general statistical approach is presented for the identification of objects in digital images, motivated by an application in aquaculture involving underwater images of fish. Using Procrustes analysis, a point distribution model is fitted on a set of training images and used as a prior distribution for the shape of a deformable template. The likelihood of a proposed template is calculated in terms of the response from a feature detector along the boundary of the template. The posterior distribution of template variables is examined by using Markov chain Monte Carlo analysis. A key challenge in the aquaculture application is the variable nature of edges arising from the surface curvature of fish and the low contrast between the foreground and background. Conventional gradient-based edge detection proves inadequate, but a parallel pattern detector copes much better. Results are presented for a fully automated analysis of the database. The strengths and weaknesses of this approach are discussed and future developments are outlined.
Type of Medium:
Online Resource
ISSN:
0035-9254
,
1467-9876
DOI:
10.1111/1467-9876.00150
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
1999
detail.hit.zdb_id:
204797-4
detail.hit.zdb_id:
1482300-7
detail.hit.zdb_id:
1476894-X
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