Geometric Total Variation for Texture Deformation

Dmitriy Bespalov, Anders Bjorholm Dahl, Ali Shokoufandeh

Abstract: In this work we propose a novel variational method that we intend to use for estimating non-rigid texture deformation. The method is able to capture variation in gray scale images with respect to the geometry of its features. Accurate localization of features in the presence of unknown deformations is a crucial property for texture characterization. Our experimental evaluations demonstrate that accounting for geometry of features in texture images leads to significant improvements in localization of these features, when textures undergo geometrical transformations. In addition, feature descriptors using geometrical total variation energies discriminate between various regular textures with accuracy comparable to SIFT descriptors, while reduced dimensionality of TVG descriptor yields significant improvements over SIFT in terms of retrieval time.