Learning efficient image representations: Connections between statistics and neuroscience
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Learning efficient image representations: Connections between statistics and neuroscience

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Learning efficient image representations: Connections between statistics and neuroscience

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dc.contributor.advisor Malo López, Jesús
dc.contributor.advisor Camps Valls, Gustavo
dc.contributor.author Laparra Pérez-Muelas, Valero
dc.contributor.other Departament de Matemàtica Aplicada ca
dc.date.accessioned 2012-04-27T10:54:56Z
dc.date.available 2012-04-27T10:54:56Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/10550/23515
dc.description.abstract This thesis summarizes different works developed in the framework of analyzing the relation between image processing, statistics and neuroscience. These relations are analyzed from the efficient coding hypothesis point of view (H. Barlow [1961] and Attneave [1954]). This hypothesis suggests that the human visual system has been adapted during the ages in order to process the visual information in an efficient way, i.e. taking advantage of the statistical regularities of the visual world. Under this classical idea different works in different directions are developed. One direction is analyzing the statistical properties of a revisited, extended and fitted classical model of the human visual system. No statistical information is used in the model. Results show that this model obtains a representation with good statistical properties, which is a new evidence in favor of the efficient coding hypothesis. From the statistical point of view, different methods are proposed and optimized using natural images. The models obtained using these statistical methods show similar behavior to the human visual system, both in the spatial and color dimensions, which are also new evidences of the efficient coding hypothesis. Applications in image processing are an important part of the Thesis. Statistical and neuroscience based methods are employed to develop a wide set of image processing algorithms. Results of these methods in denoising, classification, synthesis and quality assessment are comparable to some of the most successful current methods. en
dc.language.iso en en
dc.title Learning efficient image representations: Connections between statistics and neuroscience en
dc.type info:eu-repo/semantics/doctoralThesis en
dc.subject.unesco UNESCO::MATEMÁTICAS::Ciencia de los ordenadores en

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