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|Title:||Non-linear point distribution modelling using a multi-layer perceptron||Authors:||Sozou, Peter D.
Cootes, Timothy F.
Taylor, Chris J.
Di Mauro, E. C.
|Keywords:||Point distribution modelling;Multi-layer perceptron;Shape variation||Issue Date:||1997||Publisher:||ELsevier B. V.||Source:||Image and Vision Computing,Vol. 15, no. 6, 1997, pp. 457-463||Abstract:||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/246||ISSN:||0262-8856||DOI:||http://dx.doi.org/10.1016/S0262-8856(96)00001-7||Rights:||Copyright © 1997 Published by Elsevier Science B.V.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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