Abstract
Texture synthesis has become a well-established area. However, researchers are mostly concerned with learning the algorithm of texture synthesis to achieve higher quality and better efficiency. We hereby propose a repetitiveness metric method to pick out an optimal texture exemplar which is used to synthesize texture. Different from conventional methods of texture analysis that emphasize on texture feature analysis for the target textures, our method focuses on repetitiveness metric of texture exemplar. To achieve a more efficient method, we firstly perform a Poisson disk sampling to extract unordered texture exemplars from the input image. Using normalized cross correlation (NCC) based on fast Fourier transformation (FFT) for each exemplar, we can get some matrices. Based on repetitiveness metric, we can assign each exemplar a score. Our method can satisfy visual requirement and accomplish high-quality work in a shorter time due to FFT. Compelling visual results and computational complexity analyses prove the validity of our work.
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Acknowledgments
This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 61303101, 61572328), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846, JCYJ20170302153551588, CXZZ20140902160818443, CXZZ20140902102350474, CXZZ20150813151056544, JCYJ20150630105452814, JCYJ20160331114551175, JCYJ20160608173051207), the Start-up Research Fund of Shenzhen University (Nos. 2013-827-000009), the China-UK Visual Information Processing Laboratory (VIPL) and Maternal and child health monitoring and early warning Engineering Technology Research Center (METRC) of Guangdong Province.
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Yin, L., Lai, H., Wu, H., Wen, Z. (2018). Repetitiveness Metric of Exemplar for Texture Synthesis. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_73
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DOI: https://doi.org/10.1007/978-3-319-77383-4_73
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