Abstract
Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.
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Acknowledgements
This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 61303101 and 61572328), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846, JCYJ20170302153551588, CXZZ20140902160818443, CXZZ20140902102350474, CXZZ20150813151056544, JCYJ20150630105452814, JCYJ20160331114551175, and JCYJ20160608173051207), and the Startup Research Fund of Shenzhen University (No. 2013-827-000009).
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Huisi Wu is currently an associate professor in the College of Computer Science and Software Engineering, Shenzhen University. He received his B.Sc. and M.Sc. degrees in computer science from Xi’an Jiaotong University in 2004 and 2007, respectively. He obtained his Ph.D. degree in computer science from the Chinese University of Hong Kong in 2011. His research interests are in computer graphics, image processing, and medical imaging.
Xiaomeng Lyu received her B.S degree in software from Fujian Normal University in 2016. Currently she is studying at Shenzhen University for her master degree. Her research interests include computer vision, texture analysis and pattern recognition.
ZhenkunWen received his M.Sc degree in science and technology from Tsinghua University in 1999. Since 1987, he has been engaged in computing research and teaching in Shenzhen University. He is a professor of computing and software, and director of the Science and Technology Department of Shenzhen University. His research interests are in video tampering detection and location, video information security, and information management system design and implementation.
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Wu, H., Lyu, X. & Wen, Z. Automatic texture exemplar extraction based on global and local textureness measures. Comp. Visual Media 4, 173–184 (2018). https://doi.org/10.1007/s41095-018-0106-z
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DOI: https://doi.org/10.1007/s41095-018-0106-z