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Probabilistic models of early vision

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hoyer, Patrik O.
dc.date.accessioned 2012-02-10T09:39:56Z
dc.date.available 2012-02-10T09:39:56Z
dc.date.issued 2002-11-15
dc.identifier.isbn 951-22-6086-7
dc.identifier.issn 1456-9418
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2234
dc.description.abstract How do our brains transform patterns of light striking the retina into useful knowledge about objects and events of the external world? Thanks to intense research into the mechanisms of vision, much is now known about this process. However, we do not yet have anything close to a complete picture, and many questions remain unanswered. In addition to its clinical relevance and purely academic significance, research on vision is important because a thorough understanding of biological vision would probably help solve many major problems in computer vision. A major framework for investigating the computational basis of vision is what might be called the probabilistic view of vision. This approach emphasizes the general importance of uncertainty and probabilities in perception and, in particular, suggests that perception is tightly linked to the statistical structure of the natural environment. This thesis investigates this link by building statistical models of natural images, and relating these to what is known of the information processing performed by the early stages of the primate visual system. Recently, it was suggested that the response properties of simple cells in the primary visual cortex could be interpreted as the result of the cells performing an independent component analysis of the natural visual sensory input. This thesis provides some further support for that proposal, and, more importantly, extends the theory to also account for complex cell properties and the columnar organization of the primary visual cortex. Finally, the application of these methods to predicting neural response properties further along the visual pathway is considered. Although the models considered account for only a relatively small part of known facts concerning early visual information processing, it is nonetheless a rather impressive amount considering the simplicity of the models. This is encouraging, and suggests that many of the intricacies of visual information processing might be understood using fairly simple probabilistic models of natural sensory input. en
dc.format.extent 65, [76]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Acta polytechnica Scandinavica. Ma, Mathematics and computing series en
dc.relation.ispartofseries 117 en
dc.relation.haspart P. O. Hoyer and A. Hyvärinen, Independent component analysis applied to feature extraction from colour and stereo images, Network: Computation in Neural Systems, vol. 11, no. 3, pp. 191-210, 2000.
dc.relation.haspart A. Hyvärinen and P. O. Hoyer, Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces, Neural Computation, vol. 12, no. 7, pp. 1705-1720, 2000.
dc.relation.haspart A. Hyvärinen and P. O. Hoyer, A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images, Vision Research, vol. 41, no. 18, pp. 2413-2423, 2001.
dc.relation.haspart P. O. Hoyer, Non-negative sparse coding, in Neural Networks for Signal Processing XII (Proc. IEEE Workshop on Neural Networks for Signal Processing 2002, Martigny, Switzerland), pp. 557-565, 2002.
dc.relation.haspart P. O. Hoyer, Modeling receptive fields with non-negative sparse coding, in Computational Neuroscience: Trends in Research 2003, Elsevier, Amsterdam, 2003. In press.
dc.relation.haspart P. O. Hoyer and A. Hyvärinen, A multi-layer sparse coding network learns contour coding from natural images, Vision Research, vol. 42, no. 12, pp. 1593-1605, 2002.
dc.subject.other Computer science en
dc.subject.other Medical sciences en
dc.title Probabilistic models of early vision en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Computer Science and Engineering en
dc.contributor.department Tietotekniikan osasto fi
dc.subject.keyword natural images en
dc.subject.keyword independent component analysis en
dc.subject.keyword latent variable models en
dc.subject.keyword unsupervised learning en
dc.subject.keyword neural networks en
dc.subject.keyword early vision en
dc.subject.keyword visual cortex en
dc.identifier.urn urn:nbn:fi:tkk-002042
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Computer and Information Science en
dc.contributor.lab Informaatiotekniikan laboratorio fi

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