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Conference on Advanced Algorithms and Signal Image Processing最新文献

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High precision positioning method via robot-driven three-dimensional measurement 机器人驱动的三维测量高精度定位方法
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659724
Hua Luo, Kecheng Zhang, Junyun Shang, Meng-Long Cao, Rui Li, Na Yang, Jun Cheng
{"title":"High precision positioning method via robot-driven three-dimensional measurement","authors":"Hua Luo, Kecheng Zhang, Junyun Shang, Meng-Long Cao, Rui Li, Na Yang, Jun Cheng","doi":"10.1117/12.2659724","DOIUrl":"https://doi.org/10.1117/12.2659724","url":null,"abstract":"In the process of intelligent manufacturing, all kinds of complex workpieces in aerospace, automobile and other fields need to be measured and identified with high precision, so that industrial robots can sort or assemble the workpieces. The structure of the workpieces is complex, the surface texture is weak, and they are scattered and stacked on the automatic production line, so there are some problems such as low accuracy of three-dimensional (3D) measurement and positioning and low efficiency. To solve these problems, a high precision positioning method based on robot-driven 3D measurement is proposed. Firstly, the 3D point cloud data of the complex workpieces is obtained from the structured light 3D measurement device, and then the point cloud data is processed by the sampling consistent initial registration algorithm (SAC-IA) and the iterative nearest point algorithm (ICP). Through the rough estimation and accurate solution of the position and attitude of the workpiece, the 3D attitude of the workpieces in the coordinate system of the structured-light 3D measurement device is obtained. Finally, the spatial pose solution algorithm is used to calculate the 3D attitude of the workpieces in the robot coordinate system and guide the robot to grasp automatically. The experiments show that the grasping position error is 0.34mm, and the grasping angle error is 0.36°. It can accurately measure and identify the point cloud target, calculate the 3D attitude of the complex workpieces, and accurately guide the robot to grab the workpiece automatically, which can be popularized and applied in the industry.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127922823","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 method for generating procedural seamless textures 生成程序无缝纹理的方法
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659336
Yu-Hui Tu, Xiaolong Wang, Zefan Qi, Yinghu Liu, Xiaoxi He
{"title":"A method for generating procedural seamless textures","authors":"Yu-Hui Tu, Xiaolong Wang, Zefan Qi, Yinghu Liu, Xiaoxi He","doi":"10.1117/12.2659336","DOIUrl":"https://doi.org/10.1117/12.2659336","url":null,"abstract":"Textures are an important element to simulate real scenes. When textures are applied to each scene, the most important step to more realistic simulation of the real world is to solve the sense of repetition caused by texture tiling. Therefore, in order to simplify the process of texture image production and deal with the seams when texture tiling, this paper improves the traditional procedural texture generation algorithm based on the traditional one. First, this paper proposes an image-based method to deal with the seam problem caused by structured texture tiling, and also proposes a new algorithm to solve the texture repetition problem caused by large-scale tiling. The algorithm synthesizes infinite outputs with the same appearance using random texture blocks as inputs, with random textures, such as overlapping rocks, rocks, grass, etc. Experimental results show that the algorithm can obtain high-quality texture output while the image seamless processing steps are substantially reduced.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121593196","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
Online allocation algorithm of digital story course resources based on deep learning 基于深度学习的数字故事课程资源在线分配算法
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2660269
Ke Xu, Julina Ismail Kamal
{"title":"Online allocation algorithm of digital story course resources based on deep learning","authors":"Ke Xu, Julina Ismail Kamal","doi":"10.1117/12.2660269","DOIUrl":"https://doi.org/10.1117/12.2660269","url":null,"abstract":"The online allocation of course resources is a part of the field of cloud computing. Studying the allocation of digital story course resources through cloud computing architecture can not only meet the requirements of cloud computing resource allocation, but also improve the utilization efficiency of digital story course resources. This paper mainly proposes an application-oriented resource allocation algorithm based on deep learning. This online allocation algorithm mainly quantifies the characteristics of the data, and more accurately creates the server resources required by the digital story course. In addition, the workload prediction model is integrated into the online allocation algorithm, which can Ensure that curriculum resources and teaching are more matched, so as to give full play to the value of resource use.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"12475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130801939","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
Hidden defect detection based on metric learning 基于度量学习的隐藏缺陷检测
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659344
Ankang Liu, Lan Cheng, Yingchun Lv
{"title":"Hidden defect detection based on metric learning","authors":"Ankang Liu, Lan Cheng, Yingchun Lv","doi":"10.1117/12.2659344","DOIUrl":"https://doi.org/10.1117/12.2659344","url":null,"abstract":"Aiming at the problems that hidden defects inside objects are difficult to be visually recognized and the defect samples obtained from inspection are few, a small-sample learning detection model using ultrasonic flaw detection to extend machine vision is proposed. The model introduces an attention mechanism into the deep nearest neighbor network to adjust the image features, so that the model pays more attention to the useful defect area features, increases the amount of defect-related information, and makes full use of key defect features to detect image targets. Experiments show that the proposed method has the best performance compared with the baseline model on the self-made hidden defect dataset, and the average correct rate is up to 83.85% under 10-shot; the model is tested with noisy images, and the results show that the model detection under noisy conditions has an accuracy rate of about 76%, and it has a certain anti-noise interference ability.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114722999","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
The application of low rank matrix decomposition method in image restoration 低秩矩阵分解方法在图像恢复中的应用
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659313
Yue Peng
{"title":"The application of low rank matrix decomposition method in image restoration","authors":"Yue Peng","doi":"10.1117/12.2659313","DOIUrl":"https://doi.org/10.1117/12.2659313","url":null,"abstract":"Image restoration is a hot issue in the field of image processing. Traditional algorithms approach the original function by a regular function or remove redundant noise by similarity. This process is complicated and cumbersome. In this paper, the low rank approximate matrix of the image matrix is equivalent to the product of two smaller matrices. At the same time, the first-order and second-order statistical information of the image matrix is effectively maintained by using the matrix Frobenius norm and matrix kernel normal. Secondly, the alternating direction multiplier method is utilized to solve the model. Finally, experimental results test the effectiveness of the proposed algorithm.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124768179","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
Research on traffic sign recognition based on several machine learning methods 基于几种机器学习方法的交通标志识别研究
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659407
Haocheng Qi, Zhuohang Qin, Yue Yang, Siyuan Liu, Huilin Ren
{"title":"Research on traffic sign recognition based on several machine learning methods","authors":"Haocheng Qi, Zhuohang Qin, Yue Yang, Siyuan Liu, Huilin Ren","doi":"10.1117/12.2659407","DOIUrl":"https://doi.org/10.1117/12.2659407","url":null,"abstract":"With the in-depth development of intelligent transportation, traffic sign recognition has attracted widespread attention as an essential part of intelligent transportation. This paper studies several machine learning methods for traffic sign recognition. Through comparative analysis, it is found that Convolutional Neural Network (CNN) is superior to Support Vector Machine (SVM) and K Nearest Neighbor (KNN) methods in recognizing traffic signs. And adding Gaussian noise to the image data for enhancement can further improve the accuracy of applying a Convolutional Neural Network to identify traffic signs. The accuracy of applying a Convolutional Neural Network to identify traffic signs is 99.2%. After adding Gaussian noise with a mean of 0 and a standard deviation of 1 to the image set, the accuracy of applying a Convolutional Neural Network to identify traffic signs was increased to 99.6%. We also compared the CNN-based traffic signs recognition experiment in this paper with the experiments of two other scholars. Our experiment has higher accuracy in a particular data range and environment.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125020985","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 high-performance BIM component element detection model 高性能的BIM构件元素检测模型
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659345
Yifei Wang, Yishi Wang, Gaoda Wei
{"title":"A high-performance BIM component element detection model","authors":"Yifei Wang, Yishi Wang, Gaoda Wei","doi":"10.1117/12.2659345","DOIUrl":"https://doi.org/10.1117/12.2659345","url":null,"abstract":"IFC (Industry Foundation Classes) is a standard format for information exchange developed by Building SMART, dedicated to the collaborative work of various software in architectural design, construction and operation and maintenance. With IFC standard for various BIM (Building Information Modeling), the software provides a unified data structure and file exchange format for data exchange. However, lacking formal rigidity, data exchange is often arbitrary and prone to errors, omissions, and misrepresentations. This study applies the machine learning technique LightGBM to examine BIM elements and IFC The accuracy of the mapping between classes is extracted through feature engineering, and the BIM model element detection model is constructed. By using the BIM model training set for training, the results show that our model is more than 97.8 % accurate. And compared to the popular machine learning models, our model has higher performance.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128271135","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 digital command control protocol designed on an FPGA to control a standard railway 在FPGA上设计了一种控制标准铁路的数字命令控制协议
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2660329
Yisong Zhao
{"title":"A digital command control protocol designed on an FPGA to control a standard railway","authors":"Yisong Zhao","doi":"10.1117/12.2660329","DOIUrl":"https://doi.org/10.1117/12.2660329","url":null,"abstract":"The DCC (Digital Command Control) protocol is a standard used in model railroading to control individual locomotives or accessories by modulating the track supply voltage. The commands to the trains are generated by a DCC unit that we are going to implement in the FPGA of the Nexys card, in the form of a mixed hardware/software system Thanks to a user interface created using the buttons of the Nexys card, the FPGA must generate a digital control signal. This command must then be amplified in current using a Booster card, to obtain a signal powerful enough to be sent on the rails and then be decoded by the locomotives.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990121","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
An acceleration measure for heart rate variability via Poincaré plot 用poincarcarr图测量心率变异性的加速度
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659854
Chengwu Diao, Bin Wang
{"title":"An acceleration measure for heart rate variability via Poincaré plot","authors":"Chengwu Diao, Bin Wang","doi":"10.1117/12.2659854","DOIUrl":"https://doi.org/10.1117/12.2659854","url":null,"abstract":"In this paper, an acceleration measure (AM) for heart rate variability (HRV) is presented. Via the quantitative index AM, the acceleration of change of the normal sinus rhythm can be described effectively. Experiment results demonstrate that via the higher distribution density of experiment results of normal sinus rhythm, the sinus rhythm can be recognized and classified effectively. Hence, AM can be as a valid feature of the sinus rhythm.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122352823","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
Compression strategy of structured text based on prior dictionary for data distribution system 数据分布系统中基于先验字典的结构化文本压缩策略
Conference on Advanced Algorithms and Signal Image Processing Pub Date : 2022-11-23 DOI: 10.1117/12.2659601
Kefei Li, Yijiang Jia, Yun-Fei Ji, Baodi Xie, W. Zhang, Ping Lu, Jincai Chen
{"title":"Compression strategy of structured text based on prior dictionary for data distribution system","authors":"Kefei Li, Yijiang Jia, Yun-Fei Ji, Baodi Xie, W. Zhang, Ping Lu, Jincai Chen","doi":"10.1117/12.2659601","DOIUrl":"https://doi.org/10.1117/12.2659601","url":null,"abstract":"Data distribution service (DDS) is a middleware API standard from Object Management Group (OMG), which transfers data using a publisher-subscriber model. The number of distributed nodes deployed in today's DDS communication system can reach tens of thousands, thus improving the efficiency of the communication system is important. In this work we present a structured text encoding strategy based on prior dictionary. This compression method has considerable compression effect on structured text such as HTML. In our evaluation, the average data compression rate is reduced by 7.07%, and the average system latency is reduced by 8.61% comparing to Zlib.","PeriodicalId":335652,"journal":{"name":"Conference on Advanced Algorithms and Signal Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131517018","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
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