Perspective-aware texture analysis and synthesis

The original publication is available at www.springerlink.comInternational audienceThis paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo

A Survey on Recent Patents in Texture Synthesis

Abstract: Textures have been a research focus for many years in human perception, computer graphics and computer vision. Recently, research activities in this area emphasize on texture synthesis. Given one or more example textures, a texture synthesis algorithm generates a new one bearing the same visual characteristics. The synthesized texture can be made of arbitrary size specified by the user. It can be used in many fields such as computer animation, virtual reality, data compression, non-photorealistic rendering and so on. In this paper, the basic concepts of texture and the development process of texture synthesis techniques are introduced first. Then the recent key patents on texture synthesis schemes are reviewed. Finally, this paper points out future works in this area

Génération et édition de textures géométriques représentées par des ensembles de points

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal