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https://hdl.handle.net/20.500.14279/1952
Τίτλος: | Non-linear point distribution modelling using a multi-layer perceptron | Συγγραφείς: | Sozou, Peter D. Cootes, Timothy F. Taylor, Chris J. Di Mauro, E. C. Lanitis, Andreas |
metadata.dc.contributor.other: | Λανίτης, Ανδρέας | Major Field of Science: | Social Sciences | Λέξεις-κλειδιά: | Point distribution modelling;Multi-layer perceptron;Shape variation | Ημερομηνία Έκδοσης: | Ιου-1997 | Πηγή: | Image and Vision Computing,1997, vol. 15, no. 6, pp. 457-463 | Volume: | 15 | Issue: | 6 | Start page: | 457 | End page: | 463 | Περιοδικό: | Image and Vision Computing | Περίληψη: | 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: | https://hdl.handle.net/20.500.14279/1952 | ISSN: | 02628856 | DOI: | 10.1016/S0262-8856(96)00001-7 | Rights: | © Elsevier | Type: | Article | Affiliation: | University College London The University of Manchester Cyprus College |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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