A Robot for Artistic Painting in Authentic Colors

Каримов, А.; Kopets, Ekaterina E.; Leonov, Sergey; Scalera, Lorenzo; Butusov, Denis N.

doi:10.1007/s10846-023-01831-4

Cited by 18 publications

(10 citation statements)

References 54 publications

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“…A possible future direction could be to incorporate user guidance information into the inpainting process, which may provide more opportunities for user participation and customization. In addition, robot painting [ 44 , 45 ] is also a promising application direction. In practical applications, our image inpainting method can assist robots in better filling in missing image content.…”

Section: Discussionmentioning

confidence: 99%

Inpainting with Separable Mask Update Convolution Network

Gong

Luo

et al. 2023

Sensors

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Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this challenge, the paper proposes a novel approach called separable mask update convolution. This technique automatically learns and updates the mask, which represents the missing area, to better control the influence of invalid information within the mask area on the restoration results. Furthermore, this convolution method reduces the number of network parameters and the size of the model. The paper also introduces a regional normalization technique that collaborates with separable mask update convolution layers for improved feature extraction, thereby enhancing the quality of the restored image. Experimental results demonstrate that the proposed method performs well in restoring images with large missing areas and outperforms state-of-the-art image inpainting methods significantly in terms of image quality.

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Section: Discussionmentioning

confidence: 99%

Inpainting with Separable Mask Update Convolution Network

Gong

Luo

et al. 2023

Sensors

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“…including the reduction of complexity and computational burden in flexible multibody systems [ 12 ], dynamic reduction algorithms for flexible mechanisms [ 12 ], and the development of integral robust control algorithms for uncertain nonlinear systems [ 13 ]. Additionally, several research studies have also introduced neuroadaptive learning algorithms for robust control in constrained nonlinear systems and robotic painting systems, including studies such as [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Related Workmentioning

confidence: 99%

Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

Nasrat,

Kang,

Park

et al. 2023

Sensors

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In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.

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“…Except robotic calligraphy，more attentions have been paid to robotic painting with multicolor recently [28]- [30].…”

Section: B Neural Generator For Robotic Chinese Calligraphymentioning

confidence: 99%

“…The work [28] proposed the custom-built robot which can accurately mix artistic dyes and make it possible to reproduce colors of brushstrokes. This work designed a novel mathematical model used for artistic paint mixing, developed the principles of mixing paints.…”

Section: B Neural Generator For Robotic Chinese Calligraphymentioning

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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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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A Robot for Artistic Painting in Authentic Colors

Cited by 18 publications

References 54 publications

Inpainting with Separable Mask Update Convolution Network

Inpainting with Separable Mask Update Convolution Network

Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

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

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