a1 Department of Entomology, The Natural History Museum, Cromwell Road, London, SW7 5BD, UK:
a2 Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK:
a3 Oxford Orthopaedic Engineering Centre, Nuffield NHS Trust, Windmill Road, Oxford, OX3 7LD, UK
In this paper we describe a semi-automated digital image analysis system which is capable of discriminating five closely related species of Ichneumonidae. Specimens were distinguished by differences in their wings. The system functions by (a) extracting the significant variation (principal components) among a training set of images of the same species, (b) using these principal components to efficiently represent the morphology of wings of that species, and (c) exploiting the fact that images of the same species will share characteristic principal components, while images of different species will not. Such an approach allows the construction of modular species classifiers, to which like species correlate strongly, while dissimilar species do not. A recognition accuracy of 94% was achieved when the system was tested on 175 images of wings of the five ichneumonids. The wing images were caricatured to accentuate their venation and pigmentation patterns.
(Accepted August 21 1996)
c1 Author for correspondence.