Paint with stitches: a style definition and image-based rendering method for random-needle embroidery

Yang, Kewei; Sun, Zhengxing

doi:10.1007/s11042-017-4882-8

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…The cylinders can be further refined to incorporate the bending of a thread as it enters and leaves the fabric [GLL * 21]. The resulting visualizations yield a faithful representation of the final stitched designs [YSMY16, MS21]. The visualization can be enhanced to automatically convert input images to stitch representation where stitches are parametrized by their starting and ending locations [CSZ17, QXC * 19, QCX * 20].…”

Section: Related Workmentioning

confidence: 99%

Directionality‐Aware Design of Embroidery Patterns

Liu

Piovarči

Hafner

et al. 2023

Computer Graphics Forum

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Embroidery is a long‐standing and high‐quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine‐fabricable embroidery patterns from multi‐chromatic images equipped with user‐specified directionality fields. We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade‐off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.

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Section: Related Workmentioning

confidence: 99%

Directionality‐Aware Design of Embroidery Patterns

Liu

Piovarči

Hafner

et al. 2023

Computer Graphics Forum

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show abstract

“…However, the existing programs have been developed for regular stitches. For instance, Jie et al [38] and Yang et al [39] explored and developed random stitch embroidery, but no algorithm has been developed for loose-variegated stitch yet.…”

Section: The Computer-aided Technology For Embroiderymentioning

confidence: 99%

MasterSu: The Sustainable Development of Su Embroidery Based on Digital Technology

Zhang

et al. 2022

Sustainability

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Su embroidery, as an intangible cultural heritage of China, is a treasure accumulated by human civilization, but it has been gradually fading from people’s view in recent years. To handle the problems of slow creative output, high learning difficulty, and low production efficiency, and to promote the sustainable development of Su embroidery, this study builds an automatic generation system of Su embroidery called MasterSu, based on the CorelDraw platform. The system can automate the generation of embroidery sketches through area texture filling and color recognition, which allows users to participate in the design process. Finally, the performance and usefulness of the system are verified through user experiments, and it is confirmed that the system can facilitate novice users to understand the embroidery culture, learn the embroidery techniques, and create their embroidery works through the system.

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“…We note that embroidery with different stitch styles may require different distribution methods. Aiming at Chinese irregular needling embroidery, a series of algorithms [27,26,21] have been developed to produce results directly from input images. However, these methods cannot be generalized for other commonly used stitch styles, such as the satin stitch, etc.…”

Section: Non-parametric Texture Synthesis and Transfermentioning

confidence: 99%