Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.19266
Yongwei Zhao, Qiong Zeng, Yunhai Wang, Fan Zhong, Changhe Tu
: Traditional automatic color optimization methods face the challenge of expressing global features in dynamic data ranges. To solve this problem, an adaptive colormap optimization method based on inserting colors is proposed, which includes a process of estimating color inserting position and an inserting color optimization procedure. Firstly, color inserting positions are selected based on color discriminability and data histo-gram distribution. By keeping the color inserting positions, corresponding embedding colors are estimated through a novel energy optimization equation under the guidance of visual discriminability and the consistency to the original colormap. On the basis of the algorithm, an interactive visual data exploratory system is pro-vided, which includes supporting global data perception and local ROI analysis. The effectiveness and applica-bility of the algorithm is evaluated via a user study and a case study, based on 6 colormaps with different color features and 8 datasets with different data distributions. The results demonstrate that proposed method can produce high quality visual data information compared with other algorithms, providing a condition for further data analysis.
{"title":"Adaptive Colormap Optimization Based on Inserting Colors","authors":"Yongwei Zhao, Qiong Zeng, Yunhai Wang, Fan Zhong, Changhe Tu","doi":"10.3724/sp.j.1089.2021.19266","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.19266","url":null,"abstract":": Traditional automatic color optimization methods face the challenge of expressing global features in dynamic data ranges. To solve this problem, an adaptive colormap optimization method based on inserting colors is proposed, which includes a process of estimating color inserting position and an inserting color optimization procedure. Firstly, color inserting positions are selected based on color discriminability and data histo-gram distribution. By keeping the color inserting positions, corresponding embedding colors are estimated through a novel energy optimization equation under the guidance of visual discriminability and the consistency to the original colormap. On the basis of the algorithm, an interactive visual data exploratory system is pro-vided, which includes supporting global data perception and local ROI analysis. The effectiveness and applica-bility of the algorithm is evaluated via a user study and a case study, based on 6 colormaps with different color features and 8 datasets with different data distributions. The results demonstrate that proposed method can produce high quality visual data information compared with other algorithms, providing a condition for further data analysis.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43823062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.19264
Zhiguang Zhou, Aosheng Cheng, Shaoxiong Zhu, Guojun Li, Xiaowei Mei
: Ranking is a popular and universal approach to sort items based on the value of its attributes, which can make judicious and informed decisions effectively. This paper reviews the related research on the visual analysis for data ranking. Firstly, the design and application of visual elements such as coordinate axis location, length, angle, area and brightness/saturation from the perspective of visual element mapping is introduced. Secondly, with different structural forms of data for ranking, an overview of the advanced technologies and methods with respect to multidimensional, temporal, spatial and topological features is proposed. Furthermore, applications of ranking visual analysis in the human economy, urban traffic, culture, sports and entertainment are investigated. Finally, the challenges and future developments of ranking visualization are prospected.
{"title":"A Survey on the Visual Analytics for Data Ranking","authors":"Zhiguang Zhou, Aosheng Cheng, Shaoxiong Zhu, Guojun Li, Xiaowei Mei","doi":"10.3724/sp.j.1089.2021.19264","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.19264","url":null,"abstract":": Ranking is a popular and universal approach to sort items based on the value of its attributes, which can make judicious and informed decisions effectively. This paper reviews the related research on the visual analysis for data ranking. Firstly, the design and application of visual elements such as coordinate axis location, length, angle, area and brightness/saturation from the perspective of visual element mapping is introduced. Secondly, with different structural forms of data for ranking, an overview of the advanced technologies and methods with respect to multidimensional, temporal, spatial and topological features is proposed. Furthermore, applications of ranking visual analysis in the human economy, urban traffic, culture, sports and entertainment are investigated. Finally, the challenges and future developments of ranking visualization are prospected.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42464254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.18817
Caixia Liu, Mingqiang Wei, Yanwen Guo
{"title":"3D Point Cloud Restoration via Deep Learning: A Comprehensive Survey","authors":"Caixia Liu, Mingqiang Wei, Yanwen Guo","doi":"10.3724/sp.j.1089.2021.18817","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18817","url":null,"abstract":"","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43276396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.18816
Zhuocheng Wang, Jingqiao Zhang
{"title":"Continuous Sign Language Recognition Based on 3D Hand Skeleton Data","authors":"Zhuocheng Wang, Jingqiao Zhang","doi":"10.3724/sp.j.1089.2021.18816","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18816","url":null,"abstract":"","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44074386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.19263
Zhiwei Ai, Juelin Leng, Fang Xia, Huawei Wang, Yi Cao
The massive datasets generated by scientific or engineering simulations have reached terabytes (TB) or even petabytes (PB). Data reduction has thus become one of the most important tools for saving I/O and storage costs. In order to achieve high-precision visualization and analysis, an error-controlled data reduction approach is proposed for reducing structured large-scale datasets. Firstly, taken the difference between the resulting data and the original one as a constraint, a multi-level structured adaptively-refined background grid is constructed, according to the spatial distribution characteristics of the underlying physical fields. Secondly, the original data is interpolated and mapped to the background grid, and as a result, the data with much less cells is obtained and the storage cost is reduced. Finally, the reduced data is exported to the parallel file system in real time. The proposed data reduction algorithm is implemented based on the parallel programming framework named JASMIN. In this way, the algorithm can be directly coupled with the numerical simulation programs developed with JASMIN. Test results demonstrate that the parallel algorithm can be extended to tens of thousands of CPU cores in parallel. The proposed algorithm has been successfully applied to the electromagnetic simulation of unmanned aerial vehicle irradiation. The cell number of a structured dataset with one hundred billions cells is 1796 计算机辅助设计与图形学学报 第 33 卷 reduced by 99.8%, with the relative error less than 10%. The peak signal-tonoise ratio between the two images, rendered using the reduced data and the original one respectively, is equal to 47.08 dB, which means a high similarity and thus satisfies the precision requirement of visualization.
{"title":"Error-Controlled Data Reduction Approach for Large-Scale Structured Datasets","authors":"Zhiwei Ai, Juelin Leng, Fang Xia, Huawei Wang, Yi Cao","doi":"10.3724/sp.j.1089.2021.19263","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.19263","url":null,"abstract":"The massive datasets generated by scientific or engineering simulations have reached terabytes (TB) or even petabytes (PB). Data reduction has thus become one of the most important tools for saving I/O and storage costs. In order to achieve high-precision visualization and analysis, an error-controlled data reduction approach is proposed for reducing structured large-scale datasets. Firstly, taken the difference between the resulting data and the original one as a constraint, a multi-level structured adaptively-refined background grid is constructed, according to the spatial distribution characteristics of the underlying physical fields. Secondly, the original data is interpolated and mapped to the background grid, and as a result, the data with much less cells is obtained and the storage cost is reduced. Finally, the reduced data is exported to the parallel file system in real time. The proposed data reduction algorithm is implemented based on the parallel programming framework named JASMIN. In this way, the algorithm can be directly coupled with the numerical simulation programs developed with JASMIN. Test results demonstrate that the parallel algorithm can be extended to tens of thousands of CPU cores in parallel. The proposed algorithm has been successfully applied to the electromagnetic simulation of unmanned aerial vehicle irradiation. The cell number of a structured dataset with one hundred billions cells is 1796 计算机辅助设计与图形学学报 第 33 卷 reduced by 99.8%, with the relative error less than 10%. The peak signal-tonoise ratio between the two images, rendered using the reduced data and the original one respectively, is equal to 47.08 dB, which means a high similarity and thus satisfies the precision requirement of visualization.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42302122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.19259
Xiaoxuan Hu, S. Peng, Haijing Hou, N. Yang, Yongjie Lyu, Liang Zhou
: Traditional Chinese medicine is a profound source of Chinese culture, and studying health records is an effective means for traditional Chinese medicine inheritance and advancement. A visual analysis method is proposed for traditional Chinese medicine health records to analyze multivariate, multimodal, time-varying health record data, and studying medicines in high-dimensional symptom spaces. With multiple linked views composed by a flow chart, dimensionality reduction plots, and lab test plots, aided by brushing-and-linking in-teractions, the visual analysis method supports medical experts to practice the holistic view and the theory of syndrome differentiation in the analysis. A perception-inspired comparative visual mapping and interaction is designed to investigate the integration of traditional Chinese medicine and modern medicine. The analysis of three cases of various kidney diseases treated by a famous doctor demonstrates that proposed method is prom-ising in traditional Chinese medicine inheritance, and mining core prescriptions to design new ones.
{"title":"Visual Analysis of Traditional Chinese Medicine Health Records","authors":"Xiaoxuan Hu, S. Peng, Haijing Hou, N. Yang, Yongjie Lyu, Liang Zhou","doi":"10.3724/sp.j.1089.2021.19259","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.19259","url":null,"abstract":": Traditional Chinese medicine is a profound source of Chinese culture, and studying health records is an effective means for traditional Chinese medicine inheritance and advancement. A visual analysis method is proposed for traditional Chinese medicine health records to analyze multivariate, multimodal, time-varying health record data, and studying medicines in high-dimensional symptom spaces. With multiple linked views composed by a flow chart, dimensionality reduction plots, and lab test plots, aided by brushing-and-linking in-teractions, the visual analysis method supports medical experts to practice the holistic view and the theory of syndrome differentiation in the analysis. A perception-inspired comparative visual mapping and interaction is designed to investigate the integration of traditional Chinese medicine and modern medicine. The analysis of three cases of various kidney diseases treated by a famous doctor demonstrates that proposed method is prom-ising in traditional Chinese medicine inheritance, and mining core prescriptions to design new ones.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44076168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.3724/sp.j.1089.2021.19268
L. Ji, Yun Yang, S. Qiu, Yi Wang, Bin Tian
: Due to the problems such as strong continuity and high complexity of the data generated by the thermal power control process, patterns between strong time-dependent real-valued time series and hidden units is proposed. A case study using real power plant data is conducted to verify the effectiveness of iaRNN in assisting users to understand the working mechanism of the model and diagnose model defects.
{"title":"Visual Analytics of RNN for Thermal Power Control System Identification","authors":"L. Ji, Yun Yang, S. Qiu, Yi Wang, Bin Tian","doi":"10.3724/sp.j.1089.2021.19268","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.19268","url":null,"abstract":": Due to the problems such as strong continuity and high complexity of the data generated by the thermal power control process, patterns between strong time-dependent real-valued time series and hidden units is proposed. A case study using real power plant data is conducted to verify the effectiveness of iaRNN in assisting users to understand the working mechanism of the model and diagnose model defects.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48407072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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