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A multiresolution neural network approach to invariant image recognition

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dc.contributor.author Kollias, SD en
dc.date.accessioned 2014-03-01T01:11:36Z
dc.date.available 2014-03-01T01:11:36Z
dc.date.issued 1996 en
dc.identifier.issn 0925-2312 en
dc.identifier.uri http://hdl.handle.net/123456789/11732
dc.subject autoassociative en
dc.subject hierarchical neural networks en
dc.subject invariant en
dc.subject multiresolution image analysis en
dc.subject triple correlations en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.other Computer architecture en
dc.subject.other Computer simulation en
dc.subject.other Hierarchical systems en
dc.subject.other Image analysis en
dc.subject.other Invariance en
dc.subject.other Learning systems en
dc.subject.other Optical correlation en
dc.subject.other Optimization en
dc.subject.other Pattern recognition en
dc.subject.other Autoassociative linear networks en
dc.subject.other Hierarchical neural networks en
dc.subject.other Invariant image recognition en
dc.subject.other Multiresolution image analysis en
dc.subject.other Triple correlations en
dc.subject.other Neural networks en
dc.subject.other article en
dc.subject.other artificial neural network en
dc.subject.other image analysis en
dc.subject.other mathematical model en
dc.subject.other priority journal en
dc.title A multiresolution neural network approach to invariant image recognition en
heal.type journalArticle en
heal.identifier.primary 10.1016/0925-2312(96)00041-0 en
heal.identifier.secondary http://dx.doi.org/10.1016/0925-2312(96)00041-0 en
heal.language English en
heal.publicationDate 1996 en
heal.abstract Triple-correlation-based representations of images have recently been combined with neural network architectures to derive invariant, with respect to translation, rotation and dilation, robust classification of images. Multiresolution image analysis is used in this paper to reduce the size of these representations in an optimal way, based on autoassociative linear networks. Hierarchical neural networks are then proposed as an efficient architecture for classification or retrieval of multiresolution invariant image representations. An effective procedure for designing and training such networks is also described and simulation results are presented which illustrate the capabilities of the proposed approach.Triple-correlation-based representations of images have recently been combined with neural network architectures to derive invariant, with respect to translation, rotation and dilation, robust classification of images. Multiresolution image analysis is used in this paper to reduce the size of these representations in an optimal way, based on autoassociative linear networks. Hierarchical neural networks are then proposed as an efficient architecture for classification or retrieval of multiresolution invariant image representations. An effective procedure for designing and training such networks is also described and simulation results are presented which illustrate the capabilities of the proposed approach. en
heal.publisher Elsevier Science B.V., Amsterdam, Netherlands en
heal.journalName Neurocomputing en
dc.identifier.doi 10.1016/0925-2312(96)00041-0 en
dc.identifier.isi ISI:A1996UV61700004 en
dc.identifier.volume 12 en
dc.identifier.issue 1 en
dc.identifier.spage 35 en
dc.identifier.epage 57 en


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