Abstract
Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it’s time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.
Highlights
Texture synthesis has been a basic problem and of wide importance in computer graphics, image processing, and applications of movie or video games
In this work we propose a novel algorithm for producing perspective texture from an exemplar
We extend an energy optimization technique, i.e. the 2D texture optimization technique, to perspective texture synthesis
Summary
Texture synthesis has been a basic problem and of wide importance in computer graphics, image processing, and applications of movie or video games. Traditional example-based synthesis approaches as in [2,3] synthesize the texture either one pixel or one patch at a point in time, but having the same goal of creating visually similar images with the given example, by comparing the local neighborhoods to maintain coherence of the grown region with nearby pixels. They could keep the local continuities of the texels well. The computation efficiency of energy optimization part is highly increased
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