Abstract
Random-needle Embroidery is a graceful Chinese art designated as Intangible Cultural Heritage, which “draws” beautiful images with thousands of free-form threads. In this paper, we explore techniques for automatically translating an input image into an art image with the random-needle style. The key idea is to generate rendering primitives of this art first, from which the corresponding dictionary is learned to further sparsely code the contents in the input image. To this end, we first define the artistic style of Random-needle Embroidery by introducing the notion of “stitch”, i.e., collection of threads arranged in a certain pattern, as the basic rendering primitive. Then, we adopt sparse coding to generate a stitch dictionary which gives a compact representation of the generated stitches. During runtime, new and more image content-adaptive stitches can be synthesized by optimizing a linear combination of stitch dictionary atoms via sparse representation. Then, the synthesized stitches are placed on the canvas sequentially and connected to adjacent stitches by stitch quilting. After placing all the stitches, a blank filling strategy is proposed and adopted to fill the uncovered areas on the canvas. The experimental results show our method can generate engaging images with the random-needle style. Moreover, our rendering image is better than those obtained by using two other state-of-the art methods.
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Acknowledgements
This work was supported by National High Technology Research and Development Program of China (No. 2007AA01Z334), National Natural Science Foundation of China (Nos. 61321491 and 61272219), Innovation Fund of State Key Laboratory for Novel Software Technology (Nos. ZZKT2013A12 and ZZKT2016A11), and Program for New Century Excellent Talents in University of China (NCET-04-04605).
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Yang, K., Sun, Z. Paint with stitches: a style definition and image-based rendering method for random-needle embroidery. Multimed Tools Appl 77, 12259–12292 (2018). https://doi.org/10.1007/s11042-017-4882-8
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DOI: https://doi.org/10.1007/s11042-017-4882-8