CCD-BSM:composite-curve-dilation brush stroke model for robotic chinese calligraphy

Guo, Dongmei; Liang, Ye; Yan, Guang; Min, Huasong

doi:10.1007/s10489-022-04210-y

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…In the actual writing process, it is impossible for the brush to penetrate the surface of the paper. Based on the model proposed by Wong and IP, the brush bundle of CCD-BSM [19] was approximated as a three-dimensional inverted cone. Zhang Junsong [3] presented statistical methods to simulate the stroke with a closed area enclosed by two symmetrical 3rd Bé zier curves and applied linear regression analysis to obtain the stroke model parameters.…”

Section: A Emipirical Model Based On Virtual Brushmentioning

confidence: 99%

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CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy

Guo,

Ye,

Yan

2023

IEEE Access

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Brush stroke training models for Chinese brush play an important role in robotic Chinese calligraphy as the basis of stroke generation. How to combine the end-to-end method and physical model needs to be further clarified. As a large number of brush strokes collected by robotic arm for learning and training lacks operability. The simulated brush stroke model can be used as a source of training datasets for deep learning and training instead of real samples collected by robot. With different combinations of physical parameters of the robotic arm, a sufficient amount of sample data can be generated by the simulated brush stroke model and then to train a generator based on the datasets. In this way, we propose a composite-curvedilation brush stroke model generator (CCD-BSMG) based on our composite-curve-dilation brush stroke model (CCD-BSM), which was formed by composite curve and morphological dilation directly according to the physical parameters of robotic arm and writing posture of the brush without a large number of samples for parameter estimation. The CCD-BSMG can generate the graphics with the dataset simulated by CCD-BSM for deep learning and training. Furthermore, with the output parameters of CCD-BSMG, the images reconstructed by robotic arm can have a better performance. Compared with other stroke generative models based on disordered pixels, our generator is based on parameterized brush strokes and provides a better foundation for robotic writing in deep learning or other fields. Compared with existing model and real stroke graphics written by robots, the results of several experiments prove that the proposed CCD-BSMG can generate stroke graphics well and show that it outperformed state-of-the-art stroke models. The results demonstrate the advantages of our proposed model in terms of high similarity and especially show the robustness and efficacy.INDEX TERMS stroke training model, Chinese brush, robotic Chinese calligraphy, brush stroke neural generator

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Section: A Emipirical Model Based On Virtual Brushmentioning

confidence: 99%

“…In order to reproduce the effect of real stroke precisely, we used two steps to build the proposed simulation stroke model CCD-BSM [19]. First, without considering dilation, CCD-BSM was established the mathematical expression between brush stroke and , , ,…”

Section: A Proposed Composite-curve-dilation Based Brush Stroke Modelmentioning

confidence: 99%

CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy

Guo,

Ye,

Yan

2023

IEEE Access

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Stroke‐Seg: A deep learning‐based framework for chinese stroke segmentation

Gong,

Bai,

Nie

et al. 2024

IET Image Processing

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Chinese stroke segmentation is a crucial and challenging task for various downstream applications such as font generation, aesthetic evaluation etc. Conventional semantic segmentation techniques typically face difficulties in accurately segmenting strokes, as intersection regions in Chinese characters can belong to multiple strokes simultaneously, and these approaches often lack a holistic understanding of character composition. This paper proposes a character stroke segmentation framework named Stroke‐Seg that integrates with various semantic segmentation architectures, demonstrating adaptability to different backbone networks to tackle the above tasks. A multi‐label output strategy is proposed to effectively classify strokes in intersection areas, overcoming the limitations of traditional semantic segmentation approaches. Additionally, a prior knowledge vector is incorporated into the input layer to provide character‐specific information on stroke composition, enhancing the ability of the framework to precisely identify and segment strokes. The effectiveness of the proposed framework is demonstrated through evaluations of a comprehensive dataset (brush calligraphy stroke segmentation dataset). Evaluations show that the proposed framework significantly improves the capability of semantic segmentation networks, achieving a remarkable improvement of up to 19.9% in the true stroke rate, compared to traditional stroke segmentation techniques. The Stroke‐Seg framework integrated with TransUNet demonstrates its high performance with an impressive 98.2% true stroke rate. Furthermore, the proposed framework combined with FCN still achieves good performance while consuming the least amount of computational resources and memory, demonstrating its potential for lightweight design.

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Internal Model Control Structure Inspired Robotic Calligraphy System

Chao

Zhou

et al. 2024

IEEE Trans. Ind. Inf.

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CCD-BSM:composite-curve-dilation brush stroke model for robotic chinese calligraphy

Cited by 3 publications

References 41 publications

CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy

CCD-BSMG: Composite-Curve-Dilation-Based Brush Stroke Model Generator for Robotic Chinese Calligraphy

Stroke‐Seg: A deep learning‐based framework for chinese stroke segmentation

Internal Model Control Structure Inspired Robotic Calligraphy System

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