Action-based neural networks for effective recognition of images

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dc.contributor.author Alexopoulos, Vassilios en
dc.contributor.author Kollias, Stefanos en
dc.date.accessioned 2014-03-01T02:41:04Z
dc.date.available 2014-03-01T02:41:04Z
dc.date.issued 1995 en
dc.identifier.uri http://hdl.handle.net/123456789/30348
dc.subject Human Information Processing en
dc.subject Human Perception en
dc.subject Recurrent Neural Network en
dc.subject Neural Network en
dc.subject Time Varying en
dc.subject.other Artificial intelligence en
dc.subject.other Computer architecture en
dc.subject.other Image analysis en
dc.subject.other Learning systems en
dc.subject.other Neural networks en
dc.subject.other Object recognition en
dc.subject.other Sensory perception en
dc.subject.other Spurious signal noise en
dc.subject.other Time varying systems en
dc.subject.other Recurrent neural networks en
dc.subject.other Computer vision en
dc.title Action-based neural networks for effective recognition of images en
heal.type conferenceItem en
heal.identifier.primary 10.1109/NNSP.1995.514915 en
heal.identifier.secondary http://dx.doi.org/10.1109/NNSP.1995.514915 en
heal.publicationDate 1995 en
heal.abstract This paper presents a novel approach to the recognition of images or scenes, by associating human perception actions to them and introducing neural network architectures that are able to learn the derived representations. The approach, is related to recent research efforts towards a deeper understanding of human information processing and uses appropriate recurrent neural networks for generating the desired associations in time varying environments. Initial results obtained when applying the proposed approach to the problem of recognition of images of objects, that are deformed and/or corrupted by noise, are very encouraging. en
heal.publisher IEEE, Piscataway, NJ, United States en
heal.journalName Neural Networks for Signal Processing - Proceedings of the IEEE Workshop en
dc.identifier.doi 10.1109/NNSP.1995.514915 en
dc.identifier.spage 407 en
dc.identifier.epage 416 en

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