Non-negative Sparse Modeling of Textures
Peyré, Gabriel (2007), Non-negative Sparse Modeling of Textures, in Sgallari, Fiorella; Paragios, Nikos; Murli, Almerico, Scale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings, Springer, p. 628-639
TypeCommunication / Conférence
External document linkhttp://hal.archives-ouvertes.fr/hal-00365608/en/
Conference titleScale Space and Variational Methods in Computer Vision (SSVM'07)
Book titleScale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings
Book authorSgallari, Fiorella; Paragios, Nikos; Murli, Almerico
Series titleLecture Notes in Computer Science
Number of pages931
MetadataShow full item record
Abstract (EN)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.
Subjects / KeywordsTexture syntesis; sparsity; dictionary learning
Showing items related by title and author.