Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2最新文献_第7页

On the Role of Graph Theory Apparatus in a CAD Modeling Kernel 论图论工具在CAD建模核中的作用

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-3-70

S. Slyadnev, A. Malyshev, A. Voevodin, V. Turlapov

{"title":"On the Role of Graph Theory Apparatus in a CAD Modeling Kernel","authors":"S. Slyadnev, A. Malyshev, A. Voevodin, V. Turlapov","doi":"10.51130/graphicon-2020-2-3-70","DOIUrl":"https://doi.org/10.51130/graphicon-2020-2-3-70","url":null,"abstract":"This paper summarizes the experience of authors in solving a broad range of CAD modeling problems where the formalism of graph theory demonstrates its expressive power. Some results reported in this paper have never been published elsewhere. The set of topological and geometric heuristics backing the subgraph isomorphism algorithm is presented to achieve decent performance in our extensible feature recognition framework. By the example of sheet metal features, we show that using wise topological and geometric heuristics speeds up the search process up to interactive performance rates. For detecting CAD part’s type, we present the connected components’ analysis in the attributed adjacency graph. Our approach allows for identifying two-sided CAD parts, such as sheet metals, tubes, and flat plates. We use the notion of face transition graph for the unfoldability analysis. The basic operations on hierarchical assembly graphs are formalized in terms of graph theory for handling CAD assemblies. We describe instance singling operation that allows for addressing unique part’s occurrences in the component tree of an assembly. The presented algorithms and ideas demonstrated their efficiency and accuracy in the bunch of industrial applications developed by our team.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114743283","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: 4

Design Features of the User Interface of the Pipeline Pressure Control Monitoring System 管道压力控制监测系统用户界面设计特点

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-4-34

E. Afonina, M. N. Levaya, I. S. Levyy

引用次数: 0

Neural Network Model for Face Recognition from Dynamic Vision Sensor 基于动态视觉传感器的人脸识别神经网络模型

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-4-17

Fedor Shvetsov, Anton Konushin

引用次数: 0

Functional-Voxel Method in Problems of Geometric Modeling of Thermal Characteristics of Objects 函数体素方法在物体热特性几何建模中的应用

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-3-53

A. Plaksin, A. Tolok

引用次数: 0

Semiotic Approach in the Development of Interactive Visual Analytics Systems 交互式视觉分析系统开发中的符号学方法

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-4-12

A. Zakharova, E. Vekhter

{"title":"Semiotic Approach in the Development of Interactive Visual Analytics Systems","authors":"A. Zakharova, E. Vekhter","doi":"10.51130/graphicon-2020-2-4-12","DOIUrl":"https://doi.org/10.51130/graphicon-2020-2-4-12","url":null,"abstract":"Increasing information saturation in all spheres of life entails accumulation of large amounts of data that must be perceived, processed and decisions must be made on their basis. Therefore, the issues of interaction with data and of visual analytics require solutions at a new level. The authors investigate communication between the user and the data, highlight a number of negative trends that prevent effective use of visualization in solving practical problems, formulate aspects of subjective influence on the decision-making procedure, namely those leading to significant decrease in the efficiency of visual analytics systems. The paper proposes an approach to the problem of reasonable use of existing and potential visualization capabilities for solving data analysis problems and making control decisions. A significant factor hindering the development of visual analytics is a lack of a model for coordinated use of computational and subjective resources corresponding to the technical level of computer visualization capable of ensuring close communication between the researcher and the available data. The paper describes an approach based on the concept of visual communication, the properties of which are determined on the basis of a number of key concepts of semiotics and linguistics.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771535","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

Development of a Universal Pictographic Language Keyboard 通用象形文字键盘的研制

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-3-83

Polina Belimova, Saltanat Zhalimova, Alena Dzhumagulova

{"title":"Development of a Universal Pictographic Language Keyboard","authors":"Polina Belimova, Saltanat Zhalimova, Alena Dzhumagulova","doi":"10.51130/graphicon-2020-2-3-83","DOIUrl":"https://doi.org/10.51130/graphicon-2020-2-3-83","url":null,"abstract":"The paradigm of communication between people is changing along with global changes in the modern world. This process is related to the integration of technology into everyday life and represents an expansion of the signs and symbolic environment in the field of communication: more and more new images are appearing designed to mark the attributes of the modern world. Pictorial form of communication is a simplified, cost-effective means of communication. That is why it is widely used in education, rehabilitation, navigation systems of public spaces, web and human interaction with cyberphysical systems. However, modern pictographic systems are fragmented and poorly systematized in a constant race for compliance with the requirements of the modern world. They are almost not investigated in their practical application and are limited in their expressive possibilities. This article is devoted to the description of the initial stage of empirical research of pictographic language LoCoS and the stages of development of an alternative keyboard for communication based on it. The conclusion is made about the possibility of the existence of a universal pictographic language on the basis of its semantic and technological improvement.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124837090","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

Adversarial Learning for Effective Detector Training via Synthetic Data 基于合成数据的有效检测器训练对抗学习

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-4-16

V. Gorbachev, A. Nikitin, I. Basharov

引用次数: 1

Comparative Study of Deep Learning Models for Automatic Coronary Stenosis Detection in X-ray Angiography x线血管造影中冠状动脉狭窄自动检测的深度学习模型比较研究

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-3-75

V. Danilov, O. Gerget, K. Klyshnikov, E. Ovcharenko, Alejandro F Frangi

{"title":"Comparative Study of Deep Learning Models for Automatic Coronary Stenosis Detection in X-ray Angiography","authors":"V. Danilov, O. Gerget, K. Klyshnikov, E. Ovcharenko, Alejandro F Frangi","doi":"10.51130/graphicon-2020-2-3-75","DOIUrl":"https://doi.org/10.51130/graphicon-2020-2-3-75","url":null,"abstract":"The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, FasterRCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, FasterRCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122019237","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

Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures 基于CNN和主曲率的混合虹膜分割方法

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-3-31

Varvara Tikhonova, E. Pavelyeva

引用次数: 4

Identification and Classification of Color Textures 颜色纹理的识别与分类

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2 Pub Date : 2020-12-17 DOI: 10.51130/graphicon-2020-2-2-1

M. Mende, T. Wiener

{"title":"Identification and Classification of Color Textures","authors":"M. Mende, T. Wiener","doi":"10.51130/graphicon-2020-2-2-1","DOIUrl":"https://doi.org/10.51130/graphicon-2020-2-2-1","url":null,"abstract":"This article describes, how color textures can be reliably detected and classified in the production process independent of external parameters such as brightness, object positions (translation), angulars (rotation), object distances (scaling) or curved surfaces (rotation + scaling). The methods described here are also suitable for reliably classifying at least 18 color textures even if they differ only slightly from each other optically. The online classification of color textures is a classic task in the wood, furniture and textile industry. For example, un- wanted defects or partial soiling on moving webs can be reliably detected regard- less of fluctuations in brightness and/or shadows during process operation. Algo- rithms has been developed for teach-in with RGB-HSI-transform, set fewer seg- ments on the color textures of each class with e.g. 24x24 Pixel, use suitable transformations {HSI}, e.g. 2D-FFT for formation characteristic 2D spectral mountains in these segments, extraction of statistical features and setting up the individual classifiers. Algorithms has been developed for identification & classification in process op- eration with extraction of statistical characteristics and methods of robust classi- fication. The implementation of the methods, the triggering of the color cameras, the processing of the color information including the output of the results to the process control is done with the data analysis program Xeidana®.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128177406","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