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Sufficient and Necessary Conditions of Cubic Catmull-Rom Spline Preserving Generalized Convex Interpolation 三次Catmull-Rom样条保持广义凸插值的充要条件
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/sp.j.1089.2021.18814
Zirui Wang, Renjiang Zhang, Ma Jin
{"title":"Sufficient and Necessary Conditions of Cubic Catmull-Rom Spline Preserving Generalized Convex Interpolation","authors":"Zirui Wang, Renjiang Zhang, Ma Jin","doi":"10.3724/sp.j.1089.2021.18814","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18814","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":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43479101","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}
引用次数: 0
DD-CovidNet Model for X-Ray Images Recognition of Coronavirus Disease 2019 2019冠状病毒病X射线图像识别的DD CovidNet模型
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/SP.J.1089.2021.18791
Wei Wang, Yiyang Hu, Xin Wang, Ji Li, Yutao Li
{"title":"DD-CovidNet Model for X-Ray Images Recognition of Coronavirus Disease 2019","authors":"Wei Wang, Yiyang Hu, Xin Wang, Ji Li, Yutao Li","doi":"10.3724/SP.J.1089.2021.18791","DOIUrl":"https://doi.org/10.3724/SP.J.1089.2021.18791","url":null,"abstract":"Affected by the shortage of medical resources and low level of medical care, coronavirus disease 2019(COVID-19) has not yet been contained. It is a safe and effective way to detect infection in chest X-ray (CXR) images by deep learning. To solve the above problems, an intelligent method for automatic recognition of COVID-19 in CXR images is proposed. According to the characteristics of CXR images, a dual-path multi-scale feature fusion (DMFF) module and dense dilated depthwise separable (D3S) module are proposed to extract the shallow and deep features respectively. On this basis, an efficient and lightweight convolutional neural net-work-DD-CovidNet, is designed. DMFF module can sense more abundant spatial information by fusing multi-scale features. D3S module can extract more effective classification information by enhancing feature transfer and enlarging receptive field. The method is validated on two data sets. The experimental results show that the sensitivity of DD-CovidNet model for COVID-19 recognition is 96.08%, the precision and specificity are 100.00%, and it has less parameters and faster classification speed. Compared with other models, DD-CovidNet model has faster detection speed and more accurate detection results. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49622602","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}
引用次数: 0
A Survey on Depth Based Hand Pose Estimation 基于深度的手姿态估计研究综述
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/sp.j.1089.2021.18788
Yunlong Che, Yue Qi
{"title":"A Survey on Depth Based Hand Pose Estimation","authors":"Yunlong Che, Yue Qi","doi":"10.3724/sp.j.1089.2021.18788","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18788","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":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48734588","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}
引用次数: 0
Remote Sensing Image Colorization Based on Deep Neural Networks with Multi-Scale Residual Receptive Filed 基于多尺度残差接受域的深度神经网络遥感图像着色
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/sp.j.1089.2021.18747
Jianan Feng, Qian Jiang, Xin Jin, Shin-Jye Lee, Shanshan Huang, Shao-qing Yao
{"title":"Remote Sensing Image Colorization Based on Deep Neural Networks with Multi-Scale Residual Receptive Filed","authors":"Jianan Feng, Qian Jiang, Xin Jin, Shin-Jye Lee, Shanshan Huang, Shao-qing Yao","doi":"10.3724/sp.j.1089.2021.18747","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18747","url":null,"abstract":"To solve the problems of mistaken coloring and color bleeding in the current colorization methods, an end-to-end deep neural network is proposed to achieve remote sensing image colorization. First, the multi-scale residual receptive filed net is introduced to extract the key features of source image. Second, a color information recovery network is con-structed by using U-Net, complex residual structure, attention mechanism, sequeeze-and-excitation and pixel-shuffle blocks to obtain color result. NWPU-RESISC45 dataset is chosen for model training and validation. Compared with other color methods, the PSNR value of the proposed method is increased by 6-10 dB on average and the SSIM value is increased by 0.05-0.11. In addition, the proposed method also achieves satisfactory color results on RSSCN7 and AID datasets.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41875528","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}
引用次数: 1
Efficient Digital Waveform Compression Method for Logic Simulation of Integrated Circuits 集成电路逻辑仿真中的高效数字波形压缩方法
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/sp.j.1089.2021.18799
Yuyang Xie, Lingjie Li, Wenjian Yu
{"title":"Efficient Digital Waveform Compression Method for Logic Simulation of Integrated Circuits","authors":"Yuyang Xie, Lingjie Li, Wenjian Yu","doi":"10.3724/sp.j.1089.2021.18799","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18799","url":null,"abstract":": Circuit simulation becomes more and more important in integrated circuit design. For VLSI circuits, the simulation usually outputs signal waveforms occupying massive storage space. The compression of these signal waveforms becomes crucial to the efficiency of circuit simulation. Logic simulation mainly outputs the signal values at the time of signal transition and some auxiliary information such as signal name, signal type, signal width. A compression method for auxiliary information is proposed. Then, the signal name compression scheme in existing work is improved according to the characteristics of signal value data, and a more efficient digital waveform compression storage format is proposed. The proposed format is more adaptive to the variable-length coding for compression. At the same time, general compression algorithms can be used for secondary compression, thereby further","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44610227","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}
引用次数: 2
Edge Detection Network with Multi-Depth Feature Enhancement and Top-Level Information Guidance 具有多深度特征增强和顶层信息引导的边缘检测网络
计算机辅助设计与图形学学报 Pub Date : 2021-11-01 DOI: 10.3724/sp.j.1089.2021.18752
Wei Zhu, Kuan Cen, Xizhou Xu, Defeng He
{"title":"Edge Detection Network with Multi-Depth Feature Enhancement and Top-Level Information Guidance","authors":"Wei Zhu, Kuan Cen, Xizhou Xu, Defeng He","doi":"10.3724/sp.j.1089.2021.18752","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18752","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":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44994611","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}
引用次数: 0
Hermite-Shannon-Cosine Interval Wavelet and Its Application in Adaptive Distribute Interpolation on Curves hermite - shannon - cos区间小波及其在曲线自适应分布插值中的应用
计算机辅助设计与图形学学报 Pub Date : 2021-10-01 DOI: 10.3724/sp.j.1089.2021.18780
Kexin Meng, Meng-Zhu Liu, S. Guo, Shuli Mei
{"title":"Hermite-Shannon-Cosine Interval Wavelet and Its Application in Adaptive Distribute Interpolation on Curves","authors":"Kexin Meng, Meng-Zhu Liu, S. Guo, Shuli Mei","doi":"10.3724/sp.j.1089.2021.18780","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18780","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":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46655161","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}
引用次数: 2
Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face” 基于“梯度脸”的局部高阶主方向模式人脸识别
计算机辅助设计与图形学学报 Pub Date : 2021-10-01 DOI: 10.3724/sp.j.1089.2021.18789
Xueyi Ye, Tao Wang, Na Ying, Dingwei Qian
{"title":"Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”","authors":"Xueyi Ye, Tao Wang, Na Ying, Dingwei Qian","doi":"10.3724/sp.j.1089.2021.18789","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18789","url":null,"abstract":"Pointing to weak robustness caused by the noise sensitivity and feature redundancy of present face recognition methods with high-order features, a new method of the local high-order principal direction pattern based on “gradient face” is proposed. Firstly, the gradient face convolution operator designed is used to compute the sum of multi-directional gradient components of pixels to construct a gradient face. Then, the principal direction grouping strategy is introduced on the gradient face to characterize its high-order derivative features, and a principal direction feature map is formed according to the feature code of high-order derivatives direction changes in local neighborhood. Finally, block statistics and cascading of histogram features are made a vector to be input in to a support vector machine for multi-classification. Experimental results of several public face databases show that the proposed method is robust to changes in illumination, expression, and facial occlusion and has higher recognition efficiency.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41568329","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}
引用次数: 0
Design of Lightweight and Configurable Strong Physical Unclonable Function 轻量化可配置强物理不可控制功能的设计
计算机辅助设计与图形学学报 Pub Date : 2021-10-01 DOI: 10.3724/sp.j.1089.2021.18744
Shen Hou, Jinglong Li, Hailong Liu, Shaoqing Li, Yang Guo
{"title":"Design of Lightweight and Configurable Strong Physical Unclonable Function","authors":"Shen Hou, Jinglong Li, Hailong Liu, Shaoqing Li, Yang Guo","doi":"10.3724/sp.j.1089.2021.18744","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18744","url":null,"abstract":"To solve the problem that the physical unclonable function (PUF) structure is simple and vulnerable to modeling attacks, a strong PUF anti-attack obfuscation design based on linear feedback shift register (LFSR) is proposed. First, a fixed structure LFSR is used as a pseudo-random number generator to provide a random selection signal for the obfuscation logic. Then, a dynamic LFSR with multiple feedback polynomials is used as the obfuscation logic to obfuscate origin challenges. Finally, obfuscated challenges are loaded into the embedded PUF circuit so that the attacker cannot obtain real challenges. It improves the resistance of the PUF to modeling attacks. The proposed design is simulated by Python and FPGA. Experiments on the collected dataset show that the proposed PUF has ideal uniformity (49.8%) and uniqueness (49.9%) and keeps the same reliability. It has simple architecture and low hardware overhead and can resist a variety of modeling attacks including machine learning and deep learning.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47243132","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}
引用次数: 0
Medical Image Object Detection Algorithm for Privacy-Preserving Federated Learning 隐私保护联邦学习医学图像目标检测算法
计算机辅助设计与图形学学报 Pub Date : 2021-10-01 DOI: 10.3724/sp.j.1089.2021.18416
Sheng-sheng Wang, S. Lu, Bin Cao
{"title":"Medical Image Object Detection Algorithm for Privacy-Preserving Federated Learning","authors":"Sheng-sheng Wang, S. Lu, Bin Cao","doi":"10.3724/sp.j.1089.2021.18416","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18416","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":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47189192","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}
引用次数: 1
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