Non-negative Sparse Modeling of Textures

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. 1 Statistical Models for Texture Synthesis The characterization of textures is a central topic in computer vision and graphics, mainly approached from a probabilistic point of view. Spatial domain modeling. The works of both Efros and Leung [1] and Wei and Levoy [2] pioneered a whole area of greedy approaches to texture synthesis. These methods copy pixels one by one, enforc...

• Type: Conference
• Year: 2007
• Where: SCALESPACE
• Authors: Gabriel Peyré
• Keywords: SCALESPACE 2007, Statistical Models, Texture Synthesis, Textures