Fast Texture Synthesis for Discrete Example-Based Elements

Zhengrui Huang,Chongcheng Chen,Xiaohong Lin

doi:10.1109/access.2020.2989898

Zhengrui Huang, Chongcheng Chen + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2989898

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Abstract

Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation.

Highlights

The extraction and analysis of texture is the key issue in the technical system of computer vision [1]
Many studies and methods for texture mapping and rendering have been discussed, but it is still worthy to discuss how to realize effective texture synthesis based on different texture properties that can be divided into two classes: discrete texture and continuous texture
With a focus on fast texture synthesis for discrete texture, this paper proposes a novel framework for texturing that mainly consists of five steps

Summary

Introduction

The extraction and analysis of texture is the key issue in the technical system of computer vision [1]. Many studies and methods for texture mapping and rendering have been discussed (see [17] to [31]), but it is still worthy to discuss how to realize effective texture synthesis based on different texture properties that can be divided into two classes: discrete texture and continuous texture. For this purpose, it is necessary to propose a method that can integrate the process of feature extraction, optimization, synthesis and even stylization [3].

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