The Experts below are selected from a list of 9732 Experts worldwide ranked by ideXlab platform
Yunhe Pan - One of the best experts on this subject based on the ideXlab platform.
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computationally evaluating and reproducing the beauty of chinese Calligraphy
IEEE Intelligent Systems, 2012Co-Authors: Hao Jiang, Francis C M Lau, Yunhe PanAbstract:Training a computer to evaluate the aesthetics of Chinese characters provides a feedback mechanism to improve the quality of automatically generated Calligraphy.
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the creation process of chinese Calligraphy and emulation of imagery thinking
IEEE Intelligent Systems, 2008Co-Authors: Jun Dong, Xianjun Zhang, Yanqing Gao, Yunhe PanAbstract:Chinese Calligraphy is both an art form and the embodiment of imagery thinking. The process of Calligraphy involves studying tablets or documents, remembering the contents, and creating new artwork with calligraphic images from memory. All these steps are related to simulation intelligence, thought patterns, and cognition models. Ancient tablets were damaged by human hands and eroded by the environment, thus considerably degrading their original appearance. Automated reconstruction of the characters represented in the tablets, completed in the past by an experienced expert, is a necessary preprocessing stage that simulates the imagery thinking of Calligraphy creation. Following reconstruction, a stroke-reforming approach based on statistical models generates new calligraphic styles. We present the results of this approach and discuss ongoing problems. This article is part of a special issue on AI in China.
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automatic generation of artistic chinese Calligraphy
IEEE Intelligent Systems, 2005Co-Authors: Francis C M Lau, William K Cheung, Yunhe PanAbstract:Chinese Calligraphy is among the finest and most important of all Chinese art forms and an inseparable part of Chinese history. Its delicate aesthetic effects are generally considered to be unique among all calligraphic arts. Its subtle power is integral to traditional Chinese painting. A novel intelligent system uses a constraint-based analogous-reasoning process to automatically generate original Chinese Calligraphy that meets visually aesthetic requirements. We propose an intelligent system that can automatically create novel, aesthetically appealing Chinese Calligraphy from a few training examples of existing calligraphic styles. To demonstrate the proposed methodology's feasibility, we have implemented a prototype system that automatically generates new Chinese calligraphic art from a small training set.
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a solid model based virtual hairy brush
Computer Graphics Forum, 2002Co-Authors: Min Tang, Francis C M Lau, Yunhe PanAbstract:We present the detailed modeling of the hairy brush used typically in Chinese Calligraphy. The complex model, which includes also a model for the ink and the paper, covers the various stages of the brush going through a Calligraphy process. The model relies on the concept of writing primitives, which are the smallest units of hair clusters, to reduce the load on the simulation. Each such primitive is constructed through the general sweeping operation in CAD and described by a NURBS surface. The writing primitives dynamically adjust themselves during the virtual writing process, leaving an imprint on the virtual paper as they move. The behavior of the brush is an aggregation of the behavior of all the writing primitives. A software system based on the model has been built and tested. Samples of imitation artwork from using the system were obtained and found to be nearly indistinguishable from the real artwork.
Yueting Zhuang - One of the best experts on this subject based on the ideXlab platform.
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style consistency Calligraphy synthesis system in digital library
ACM IEEE Joint Conference on Digital Libraries, 2009Co-Authors: Yueting ZhuangAbstract:There are lots of digitized Calligraphy works written by ancient famous calligraphists in CADAL (China-America Digital Academic Library) digital library. To make use of these resources, users want to generate a tablet or a piece of calligraphic works written by some ancient famous calligraphist. But some characters in the tablet or the calligraphic work hadn't been written by the calligraphist or though were ever written but are hard to read because of long time weathering. In this paper, a novel approach is proposed to synthesize Chinese calligraphic characters which are in the same style of some calligraphist, and a corresponding system is developed for Calligraphy works generation and tablets design. Calligraphic character is represented by a three-level hierarchical model. A novel approach for determining the character structure is proposed, which takes advantage of both the structure of the same characters of different styles and the structure of similar characters of the same style. A style evaluation model (SEM) is presented to evaluate whether the calligraphic character generated is in the same style of the specified calligraphist and to adjust the calligraphic character generated. Our experiments show that this system is effective.
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latent style model discovering writing styles for Calligraphy works
Journal of Visual Communication and Image Representation, 2009Co-Authors: Yueting ZhuangAbstract:Chinese Calligraphy works is a valuable part of the Chinese culture heritage. More and more Calligraphy works images are digitized, preserved and exhibited in digital library. Users always want to appreciate the style-similar works simultaneously. To satisfy their need, calligraphic style representation and browsing Calligraphy works by its style are the most important problems to be addressed. This paper proposes calligraphic style representation which is a multinomial probability distribution over visual words, and Latent Style Model to discover the style of Calligraphy works and organize the works by style. In our experiments, we evaluated various factors that influence the model, and proved the effectiveness of the style representation and the model. At last, we illustrate the Calligraphic Style Browser to organize and exhibit the resource according to the styles.
Chihmin Lin - One of the best experts on this subject based on the ideXlab platform.
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generative adversarial nets in robotic chinese Calligraphy
International Conference on Robotics and Automation, 2018Co-Authors: Fei Chao, Dajun Zhou, Longzhi Yang, Chihmin Lin, Changjing Shang, Changle ZhouAbstract:Conventional approaches of robotic writing of Chinese character strokes often suffer from limited font generation methods, and thus the writing results often lack of diversity. This has seriously restricted the high quality writing ability of robots. This paper proposes a generative adversarial nets-based calligraphic robotic framework, which enables a robot to learn writing fundamental Chinese strokes with rich diversity and good originality. In particular, the framework considers the learning process of robotic writing as an adversarial procedure which is implemented by three interactive modules including a stroke generation module, a stroke discriminative module and a training module. Noting that the stroke generative module included in the conventional generative adversarial nets cannot solve the non-differentiable problem, the policy gradient commonly used in reinforcement learning is thus adapted in this work to train the generative module by regarding the outputs from the discriminative module as rewards. Experimental results demonstrate that the proposed framework allows a calligraphic robot to successfully write fundamental Chinese strokes with good quality in various styles. The experiment also suggests the proposed approach can achieve human-level stroke writing quality without the requirement of a performance evaluation system. This approach therefore significantly boosts the robotic autonomous creation ability.
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a robot Calligraphy system from simple to complex writing by human gestures
Engineering Applications of Artificial Intelligence, 2017Co-Authors: Fei Chao, Longzhi Yang, Changle Zhou, Chihmin Lin, Changjing Shang, Yuxuan Huang, Xin ZhangAbstract:Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot Calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot Calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.
Changle Zhou - One of the best experts on this subject based on the ideXlab platform.
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generative adversarial nets in robotic chinese Calligraphy
International Conference on Robotics and Automation, 2018Co-Authors: Fei Chao, Dajun Zhou, Longzhi Yang, Chihmin Lin, Changjing Shang, Changle ZhouAbstract:Conventional approaches of robotic writing of Chinese character strokes often suffer from limited font generation methods, and thus the writing results often lack of diversity. This has seriously restricted the high quality writing ability of robots. This paper proposes a generative adversarial nets-based calligraphic robotic framework, which enables a robot to learn writing fundamental Chinese strokes with rich diversity and good originality. In particular, the framework considers the learning process of robotic writing as an adversarial procedure which is implemented by three interactive modules including a stroke generation module, a stroke discriminative module and a training module. Noting that the stroke generative module included in the conventional generative adversarial nets cannot solve the non-differentiable problem, the policy gradient commonly used in reinforcement learning is thus adapted in this work to train the generative module by regarding the outputs from the discriminative module as rewards. Experimental results demonstrate that the proposed framework allows a calligraphic robot to successfully write fundamental Chinese strokes with good quality in various styles. The experiment also suggests the proposed approach can achieve human-level stroke writing quality without the requirement of a performance evaluation system. This approach therefore significantly boosts the robotic autonomous creation ability.
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a computational evaluation system of chinese Calligraphy via extended possibility probability distribution method
International Conference on Natural Computation, 2017Co-Authors: Dajun Zhou, Fei Chao, Longzhi Yang, Changle ZhouAbstract:Robotic Calligraphy has became a popular research topic in robotics. Therefore, a computational Calligraphy evaluation system is required to access the quality of robotic writing results. This paper applies three types of feature criteria, derived from Chinese Calligraphy theories, to extract features of Chinese characters from Chinese Calligraphy textbooks. Then, the Possibility-Probability Distribution method deals with these extracted features, so as to obtain the feature distribution of quality handwriting characters. The Possibility-Probability Distribution method uses the extracted features to automatically build an interior-outer-set computational model based on information diffusion theory. When the computational model is established, each Chinese character, written by a robot, is also extracted to three features; then, the computational model estimates each character's evaluation value. The experimental results demonstrate that the proposed method successfully produces an interior-outer-set computational model from Chinese Calligraphy books. In particular, the model is able to generate an evaluation result for each character written by a robot system. To check the validation of the computational model, these characters are also evaluated by human experts. The comparison shows that the evaluation results of human experts are very similar to that of the computational model.
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a robot Calligraphy system from simple to complex writing by human gestures
Engineering Applications of Artificial Intelligence, 2017Co-Authors: Fei Chao, Longzhi Yang, Changle Zhou, Chihmin Lin, Changjing Shang, Yuxuan Huang, Xin ZhangAbstract:Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot Calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot Calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.
Fei Chao - One of the best experts on this subject based on the ideXlab platform.
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generative adversarial nets in robotic chinese Calligraphy
International Conference on Robotics and Automation, 2018Co-Authors: Fei Chao, Dajun Zhou, Longzhi Yang, Chihmin Lin, Changjing Shang, Changle ZhouAbstract:Conventional approaches of robotic writing of Chinese character strokes often suffer from limited font generation methods, and thus the writing results often lack of diversity. This has seriously restricted the high quality writing ability of robots. This paper proposes a generative adversarial nets-based calligraphic robotic framework, which enables a robot to learn writing fundamental Chinese strokes with rich diversity and good originality. In particular, the framework considers the learning process of robotic writing as an adversarial procedure which is implemented by three interactive modules including a stroke generation module, a stroke discriminative module and a training module. Noting that the stroke generative module included in the conventional generative adversarial nets cannot solve the non-differentiable problem, the policy gradient commonly used in reinforcement learning is thus adapted in this work to train the generative module by regarding the outputs from the discriminative module as rewards. Experimental results demonstrate that the proposed framework allows a calligraphic robot to successfully write fundamental Chinese strokes with good quality in various styles. The experiment also suggests the proposed approach can achieve human-level stroke writing quality without the requirement of a performance evaluation system. This approach therefore significantly boosts the robotic autonomous creation ability.
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a computational evaluation system of chinese Calligraphy via extended possibility probability distribution method
International Conference on Natural Computation, 2017Co-Authors: Dajun Zhou, Fei Chao, Longzhi Yang, Changle ZhouAbstract:Robotic Calligraphy has became a popular research topic in robotics. Therefore, a computational Calligraphy evaluation system is required to access the quality of robotic writing results. This paper applies three types of feature criteria, derived from Chinese Calligraphy theories, to extract features of Chinese characters from Chinese Calligraphy textbooks. Then, the Possibility-Probability Distribution method deals with these extracted features, so as to obtain the feature distribution of quality handwriting characters. The Possibility-Probability Distribution method uses the extracted features to automatically build an interior-outer-set computational model based on information diffusion theory. When the computational model is established, each Chinese character, written by a robot, is also extracted to three features; then, the computational model estimates each character's evaluation value. The experimental results demonstrate that the proposed method successfully produces an interior-outer-set computational model from Chinese Calligraphy books. In particular, the model is able to generate an evaluation result for each character written by a robot system. To check the validation of the computational model, these characters are also evaluated by human experts. The comparison shows that the evaluation results of human experts are very similar to that of the computational model.
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a robot Calligraphy system from simple to complex writing by human gestures
Engineering Applications of Artificial Intelligence, 2017Co-Authors: Fei Chao, Longzhi Yang, Changle Zhou, Chihmin Lin, Changjing Shang, Yuxuan Huang, Xin ZhangAbstract:Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot Calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot Calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.
