计算机辅助设计与图形学学报Pub Date : 2021-09-01DOI: 10.3724/sp.j.1089.2021.18366
Liangji Chen, Fei Gao, Bo Zhao, Longfei Ma
{"title":"Non-Uniform B-Spline Curve Interpolation Method for Feature Points Selection under Curvature Adaptive Condition","authors":"Liangji Chen, Fei Gao, Bo Zhao, Longfei Ma","doi":"10.3724/sp.j.1089.2021.18366","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18366","url":null,"abstract":": Aiming at the massive discrete tool position data generated in the computer numerically controlled programming stage, a non-uniform B-spline curve interpolation method based on curvature adaptive select-ing feature point is proposed under the condition of satisfying erpolation curve can meet the interpolation accuracy condition. The simulation calculation results of the ac-tual tool position data show that the method can better retain the characteristics of the original data curve in terms of shape and accuracy even when a large amount of the original tool position data is removed, and it has advantages of fewer iteration calculations and larger removing the data points. The method will have high application value to the spline computer numerically controlled programming of massive discrete tool position data.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42955021","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}
计算机辅助设计与图形学学报Pub Date : 2021-09-01DOI: 10.3724/sp.j.1089.2021.18822
Pei Lyu, Weichao Chen, Quan Zhang, Mingliang Xu, Long Huang, Chenbing Guo, Bing Zhou
{"title":"The Design and Optimization of Ship Cabin Space Layout Based on Crowd Simulation","authors":"Pei Lyu, Weichao Chen, Quan Zhang, Mingliang Xu, Long Huang, Chenbing Guo, Bing Zhou","doi":"10.3724/sp.j.1089.2021.18822","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18822","url":null,"abstract":"","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41804903","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}
计算机辅助设计与图形学学报Pub Date : 2021-08-01DOI: 10.3724/sp.j.1089.2021.18660
Chen Bao, Yunhai Wang
{"title":"Local Banking to 45°: Axis Grid Generation for Line Charts","authors":"Chen Bao, Yunhai Wang","doi":"10.3724/sp.j.1089.2021.18660","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18660","url":null,"abstract":"","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42429019","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}
计算机辅助设计与图形学学报Pub Date : 2021-08-01DOI: 10.3724/sp.j.1089.2021.18687
Yuxuan Hou, Yining Di, Zhong Ren, Y. Tao, Wei Chen
{"title":"Machine Learning Methods in Medical Image Compression","authors":"Yuxuan Hou, Yining Di, Zhong Ren, Y. Tao, Wei Chen","doi":"10.3724/sp.j.1089.2021.18687","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18687","url":null,"abstract":"A large amount of image data such as CT that needs storage and transmission is generated in medical research. It is hard for the hospital to handle all data of the numerous patients. Therefore, it is of vital importance to compress these image data. Recently, learning-based medical image compression has become a new research trend with the development of artificial intelligence. Traditional methods in medical data compression are firstly reviewed. Further study in learning-based approaches is made, and the compression performance of these approaches in different medical image data such as brain CT and liver CT are shown. In the meantime, the advantages and disadvantages of these approaches in various aspects such as compression ratio, algorithm complexity and reconstruction quality are systematically summarized. It is pointed out that the combination of learning-based method and ROI-based method achieves high compression ratio brought by lossy compression, while keeping the feature information of the critical regions. Consequently, this approach is much more suitable for medical image compression than others. Finally, the paper concluded with a discussion of future development in this field.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47629041","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}
计算机辅助设计与图形学学报Pub Date : 2021-08-01DOI: 10.3724/sp.j.1089.2021.18679
Le Tu, Binjie Chen, Zhiguang Zhou
{"title":"A Survey on OD Data Visualization","authors":"Le Tu, Binjie Chen, Zhiguang Zhou","doi":"10.3724/sp.j.1089.2021.18679","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18679","url":null,"abstract":"","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46711145","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}
计算机辅助设计与图形学学报Pub Date : 2021-07-01DOI: 10.3724/sp.j.1089.2021.18625
Baoqi Zhao, Fei Yu, Junmei Sun, Xiumei Li, L. Yuan, Lei Xiao
{"title":"Glandular Cell Segmentation Method Combined with Dense Connective Blocks and Self-Attention Mechanism","authors":"Baoqi Zhao, Fei Yu, Junmei Sun, Xiumei Li, L. Yuan, Lei Xiao","doi":"10.3724/sp.j.1089.2021.18625","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18625","url":null,"abstract":"Currently used cell segmentation methods are easily to cause the problem of missegmentation and impreciseness for glandular cell segmentation. A glandular cell segmentation model based on U-Net network is proposed which combines dense connective blocks and self-attention mechanism. Firstly, the convolution layers in the U-Net structure are combined to form the dense connective blocks, so that the information can be extracted from the image at different scales. Then the self-attention mechanism is introduced at the decoder to establish a rich context-dependent model for local features to suppress unnecessary feature propagation and improve the accuracy of glandular cell segmentation. The experimental results on the 2015 MICCAI Gland Segmentation Challenge dataset show that the proposed model, with a small number of extra parameters, can achieve improved performance in terms of F1-score, Mean Dice coefficient, and Hausdorff distance compared with other U-Net based methods.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46043607","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}
{"title":"Improved Efficient Convolutional Neural Networks for Complex Scene Mask-Wearing Detection","authors":"Junxiao Xue, Junjin Cheng, Qibin Zhang, Yibo Guo, Aiguo Lu, Jian Li, Xi Wan, Jing Xu","doi":"10.3724/sp.j.1089.2021.18635","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18635","url":null,"abstract":": To solve the problem about low accuracy of mask wear detection under complex lighting and face lean conditions, a method of mask wear detection under intricate environment using efficient convolutional neural network is proposed, which uses pre-training such as hard negative mining to learn more samples of face feature, utilize multi-task convolutional neural networks (MTCNN) to estimate the possibility of face information, and get accurate face location. With attention mechanism in feature pyramid network, enhanc-ing the weight of key points on human face, employing efficient neural network detection will be wore on mask-wearing detection as a simple binary classification problem. Under the environment of TensorFlow platform, not only data training, data preprocessing, but also the contrast experiment with AIZOO method are completed. A data set containing with 816 images is collected, marked and trained. During the data pre-processing, images are set as fixed size to reduce the amount of computation and promote the detection speed. Then, image enhancement algorithm is used to conduct distortion processing to improve the robust-ness of this model. On this basis, MTCNN is used to detect the face information in pictures, modify and normalize all data, then put them into neural network and the trained model to detection. The experimental results show that under complex conditions such as complex lighting and face tilt, the accuracy can reach 83% and 91% respectively, which means can accurately detect whether wearing a mask.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46256511","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}