Evaluating Brush Movements for Chinese Calligraphy: A Computer Vision Based Approach
Authors: Pengfei Xu, Lei Wang, Ziyu Guan, Xia Zheng, Xiaojiang Chen, Zhanyong Tang, Dingyi Fang, Xiaoqing Gong, Zheng Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly accurate in identifying brush movements, yielding an average accuracy of 90%, and the generated score is within 3% of errors when compared to the one given by human experts. |
| Researcher Affiliation | Academia | 1 School of Information Science and Technology, Northwest University, China 2 Department of Cultural Heritage and Museology, Zhejiang University, China 3 Meta Lab, School of Computing and Communications, Lancaster University, U. K. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://goo.gl/X1J2q R. |
| Open Datasets | Yes | Code and data are available at https://goo.gl/X1J2q R. |
| Dataset Splits | No | We randomly select 300 videos from the whole set as the training data by labeling the frame sequences of SBTP, writing a stroke and LBFP, and the remaining videos are used as the test data.This process is repeated 10 times. |
| Hardware Specification | Yes | Our approach runs on a workstation with an Intel E5 CPU, a Nvidia GTX 1080 GPU and 64GB of RAM. |
| Software Dependencies | No | The paper mentions software components like 'fast RCNN', 'TLD', 'DTW', 'MCNN-LSTM', 'CNN', but does not provide specific version numbers for any of these or other ancillary software dependencies. |
| Experiment Setup | Yes | The structure of convolution layers in MCNN-LSTM is design as mentioned above, batch size is 50 and learning rate is set as 0.001. |