@inproceedings{b022bad5e1404be5b9529d10cb1f0855,
title = "Multiscale texture orientation analysis using spectral total-variation decomposition",
abstract = "Multi-level texture separation can considerably improve texture analysis, a significant component in many computer vision tasks. This paper aims at obtaining precise local texture orientations of images in a multiscale manner, characterizing the main obvious ones as well as the very subtle ones. We use the total variation spectral framework to decompose the image into its different textural scales. Gabor filter banks are then employed to detect prominent orientations within the multiscale representation. A necessary condition for perfect texture separation is given, based on the spectral total-variation theory. We show that using this method we can detect and differentiate a mixture of overlapping textures and obtain with high fidelity a multi-valued orientation representation of the image.",
keywords = "Image decomposition, Image enhancement, Nonlinear eigenfunction analysis, Spectral total variation, Total variation",
author = "Dikla Horesh and Guy Gilboa",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015 ; Conference date: 31-05-2015 Through 04-06-2015",
year = "2015",
doi = "10.1007/978-3-319-18461-6\_39",
language = "الإنجليزيّة",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "486--497",
editor = "Mila Nikolova and Jean-Fran{\c c}ois Aujol and Nicolas Papadakis",
booktitle = "Scale Space and Variational Methods in Computer Vision - 5th International Conference, SSVM 2015, Proceedings",
}