Non-linear point distribution modelling using a multi-layer perceptron

P. D. Sozou, T. F. Cootes, C. J. Taylor, E. C. Di Mauro, A. Lanitis

    Research output: Contribution to journalArticlepeer-review


    Objects of the same class sometimes exhibit variation in shape. This shape variation has previously been modelled by means of point distribution models (PDMs) in which there is a linear relationship between a set of shape parameters and the positions of points on the shape. A polynomial regression generalization of PDMs, which succeeds in capturing certain forms of non-linear shape variability, has also been described. Here we present a new form of PDM, which uses a multi-layer perceptron to carry out non-linear principal component analysis. We compare the performance of the new model with that of the existing models on two classes of variable shape: one exhibits bending, and the other exhibits complete rotation. The linear PDM fails on both classes of shape; the polynomial regression model succeeds for the first class of shapes but fails for the second; the new multi-layer perceptron model performs well for both classes of shape. The new model is the most general formulation for PDMs which has been proposed to date. © 1997 Elsevier Science B.V.
    Original languageEnglish
    Pages (from-to)457-463
    Number of pages6
    JournalImage and Vision Computing
    Issue number6
    Publication statusPublished - Jun 1997


    • Multi-layer perceptron
    • Point distribution modelling
    • Shape variation


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