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dc.creatorNawel Takouachet
dc.creatorSamuel Delepoulle
dc.creatorChristophe Renaud
dc.creatorNesrine Zoghlami
dc.creatorJoão Manuel R. S. Tavares
dc.description.abstractUnbiased global illumination methods based on stochastical techniques provide photorealistic images. However, they are prone to noise that can only be reduced by increasing the number of processed samples. The problem of finding the number of samples that are required in order to ensure that most observers cannot perceive any noise is still an open issue. In this article, we address this problem focusing on visual perception of noise. However, rather than using known perceptual models, we investigate the use of learning approaches classically used in the field of Artificial Intelligence. Hence, we propose to use such approaches to create a model which is able to learn which image highlights perceptual noise. The learning is performed through the use of a database of examples based on experimentations of noise perception with human users. This model can then be used in any progressive stochastic global illumination method in order to find the visual convergence threshold of different parts of an input image.
dc.subjectCiências Tecnológicas, Ciências da engenharia e tecnologias
dc.subjectTechnological sciences, Engineering and technology
dc.titlePerception of noise and Global Illumination: Toward an automatic stopping criterion based on SVM
dc.typeArtigo em Revista Científica Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.subject.fosCiências da engenharia e tecnologias
dc.subject.fosEngineering and technology
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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