Perspective-aware texture analysis and synthesis

Content-Aware Texture Synthesis

Existing example-based texture synthesis techniques are inherently unadapted to textures consisting of a set of randomly disposed, individually discernible shapes. Local methods striving at pixel-based discontinuity reduction hardly preserve input’s long-range structures. Alternatively, research built upon the supposed respect by the input’s features of given placement rules are too restrictive...

Texture Synthesis for Perspective Example

Before the final report, I gives a report about some progress after the presentation on November 20th. The first part is to optimize current algorithm’s time and space complexity. I am rewriting the KD-tree data structure used in patch match process. There are two main differences. First, in previous KD-tree implementation, dim-n vectors are stored in the KD-tree locally, which makes the space ...

A Shape-Aware Model for Discrete Texture Synthesis

We present a novel shape-aware method for synthesizing 2D and 3D discrete element textures consisting of collections of distinct vector graphics objects. Extending the long-proven point process framework, we propose a shape process, a novel stochastic model based on spatial measurements that fully take into account the geometry of the elements. We demonstrate that our approach is well-suited fo...

Video Coding Using Texture Analysis and Synthesis

A new approach to video coding is presented, where video scenes are classified into textures with subjectively relevant and irrelevant details. We apply this idea to improve video coding by using a texture analyzer and a texture synthesizer. The analyzer identifies the texture regions with no important subjective details and generates coarse masks as well as side information for the synthesizer...

Projet PERSEE Texture analysis and synthesis