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https://hdl.handle.net/20.500.14279/1952
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Sozou, Peter D. | - |
dc.contributor.author | Cootes, Timothy F. | - |
dc.contributor.author | Taylor, Chris J. | - |
dc.contributor.author | Di Mauro, E. C. | - |
dc.contributor.author | Lanitis, Andreas | - |
dc.contributor.other | Λανίτης, Ανδρέας | - |
dc.date.accessioned | 2009-05-28T12:28:38Z | en |
dc.date.accessioned | 2013-05-16T13:11:00Z | - |
dc.date.accessioned | 2015-12-02T09:41:03Z | - |
dc.date.available | 2009-05-28T12:28:38Z | en |
dc.date.available | 2013-05-16T13:11:00Z | - |
dc.date.available | 2015-12-02T09:41:03Z | - |
dc.date.issued | 1997-06 | - |
dc.identifier.citation | Image and Vision Computing,1997, vol. 15, no. 6, pp. 457-463 | en_US |
dc.identifier.issn | 02628856 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1952 | - |
dc.description.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. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Image and Vision Computing | en_US |
dc.rights | © Elsevier | en_US |
dc.subject | Point distribution modelling | en_US |
dc.subject | Multi-layer perceptron | en_US |
dc.subject | Shape variation | en_US |
dc.title | Non-linear point distribution modelling using a multi-layer perceptron | en_US |
dc.type | Article | en_US |
dc.collaboration | University College London | en_US |
dc.collaboration | The University of Manchester | en_US |
dc.collaboration | Cyprus College | en_US |
dc.journals | Subscription | en_US |
dc.country | Cyprus | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Social Sciences | en_US |
dc.identifier.doi | 10.1016/S0262-8856(96)00001-7 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 6 | en_US |
dc.relation.volume | 15 | en_US |
cut.common.academicyear | 2020-2021 | en_US |
dc.identifier.spage | 457 | en_US |
dc.identifier.epage | 463 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 0262-8856 | - |
crisitem.journal.publisher | Elsevier | - |
crisitem.author.dept | Department of Multimedia and Graphic Arts | - |
crisitem.author.faculty | Faculty of Fine and Applied Arts | - |
crisitem.author.orcid | 0000-0001-6841-8065 | - |
crisitem.author.parentorg | Faculty of Fine and Applied Arts | - |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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