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Non-negative Sparse Modeling of Textures

  • Conference paper
Scale Space and Variational Methods in Computer Vision (SSVM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4485))

Abstract

This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the variances and kurtosis of the marginals of the decomposition of patches in the learned dictionary. A fast sampling algorithm allows to draw a typical image from this model. The resulting texture synthesis captures the geometric features of the original exemplar. To speed up synthesis and generate structures of various sizes, a multi-scale process is used. Applications to texture synthesis, image inpainting and texture segmentation are presented.

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Fiorella Sgallari Almerico Murli Nikos Paragios

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Peyré, G. (2007). Non-negative Sparse Modeling of Textures. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_54

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  • DOI: https://doi.org/10.1007/978-3-540-72823-8_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72822-1

  • Online ISBN: 978-3-540-72823-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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