Computer Science Research Notes最新文献_第4页

Real-Time Visual Analytics for Remote Monitoring of Patient’s Health 用于远程监测患者健康的实时可视化分析

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.61

Maryam Boumrah, S. Garbaya, A. Radgui

引用次数: 0

Semi-Supervised Learning Approach for Fine Grained Human Hand Action Recognition in Industrial Assembly 工业装配中细粒度人手动作识别的半监督学习方法

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.58

Fabian Sturm, Rahul Sathiyababu, E. Hergenroether, M. Siegel

{"title":"Semi-Supervised Learning Approach for Fine Grained Human Hand Action Recognition in Industrial Assembly","authors":"Fabian Sturm, Rahul Sathiyababu, E. Hergenroether, M. Siegel","doi":"10.24132/csrn.3301.58","DOIUrl":"https://doi.org/10.24132/csrn.3301.58","url":null,"abstract":"Until now, it has been impossible to imagine industrial manual assembly without humans due to their flexibility and adaptability. But the assembly process does not always benefit from human intervention. The error-proneness of the assembler due to disturbance, distraction or inattention requires intelligent support of the employee and is ideally suited for deep learning approaches because of the permanently occurring and repetitive data patterns. However, there is the problem that the labels of the data are not always sufficiently available. In this work, a spatio-temporal transformer model approach is used to address the circumstances of few labels in an industrial setting. A pseudo-labeling method from the field of semi-supervised transfer learning is applied for model training, and the entire architecture is adapted to the fine-grained recognition of human hand actions in assembly. This implementation significantly improves the generalization of the model during the training process over different variations of strong and weak classes from the ground truth and proves that it is possible to work with deep learning technologies in an industrial setting, even with few labels. In addition to the main goal of improving the generalization capabilities of the model by using less data during training and exploring different variations of appropriate ground truth and new classes, the recognition capabilities of the model are improved by adding convolution to the temporal embedding layer, which increases the test accuracy by over 5% compared to a similar predecessor model.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064208","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

Error-Robust Indoor Augmented Reality Navigation: Evaluation Criteria and a New Approach 误差鲁棒室内增强现实导航:评估标准与新方法

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.17

Oliver Scheibert, Jannis Möller, S. Grogorick, M. Eisemann

引用次数: 0

Raytracing Renaissance: An elegant framework for modeling light at Multiple Scales 光线追踪文艺复兴:一个优雅的框架，用于在多个尺度上建模光线

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.2

S. Semwal

{"title":"Raytracing Renaissance: An elegant framework for modeling light at Multiple Scales","authors":"S. Semwal","doi":"10.24132/csrn.3301.2","DOIUrl":"https://doi.org/10.24132/csrn.3301.2","url":null,"abstract":"Ray tracing remains of interest to Computer Graphics community with its elegant framing of how light interacts with objects, being able to easily support multiple light sources, and simple framework of merging synthetic and real cameras. Recent trends to provide implementations at the chip-level means raytracing’s constant quest of realism would propel its usage in real-time applications. AR/VR, Animations, 3DGames Industry, 3D-large scale simulations, and future social computing platforms are just a few examples of possible major impact. Raytracing is also appealing to HCI community because raytracing extends well along the 3D-space and time, seamlessly blending both synthetic and real cameras at multiple scales to support storytelling. This presentation will include a few milestones from my work such as the Slicing Extent technique and Directed Safe Zones. Our recent applications of applying machine learning techniques creating novel synthetic views, which could also provide a future doorway to handle dynamic scenes with more compute power as needed, will also be presented. It is once again renaissance for ray tracing which for last 50+ years has remained the most elegant technique for modeling light phenomena in virtual worlds at whatever scale compute power could support.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043618","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 Method of Mixed States for Interactive Editing of Big Point Clouds 大点云交互编辑的混合状态方法

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.21

W. Benger, A. Voicu, R. Baran, Loredana Gonciulea, Cosmin Barna, F. Steinbacher

引用次数: 0

Sex Classification of Face Images using Embedded Prototype Subspace Classifiers 基于嵌入式原型子空间分类器的人脸图像性别分类

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.7

A. Hast

{"title":"Sex Classification of Face Images using Embedded Prototype Subspace Classifiers","authors":"A. Hast","doi":"10.24132/csrn.3301.7","DOIUrl":"https://doi.org/10.24132/csrn.3301.7","url":null,"abstract":"In recent academic literature Sex and Gender have both become synonyms, even though distinct definitions do exist. This give rise to the question, which of those two are actually face image classifiers identifying? It will be argued and explained why CNN based classifiers will generally identify gender, while feeding face recognition feature vectors into a neural network, will tend to verify sex rather than gender. It is shown for the first time how state of the art Sex Classification can be performed using Embedded Prototype Subspace Classifiers (EPSC) and also how the projection depth can be learned efficiently. The automatic Gender classification, which is produced by the emph{InsightFace} project, is used as a baseline and compared to the results given by the EPSC, which takes the feature vectors produced by emph{InsightFace} as input. It turns out that the depth of projection needed is much larger for these face feature vectors than for an example classifying on MNIST or similar. Therefore, one important contribution is a simple method to determine the optimal depth for any kind of data. Furthermore, it is shown how the weights in the final layer can be set in order to make the choice of depth stable and independent of the kind of learning data. The resulting EPSC is extremely light weight and yet very accurate, reaching over $98%$ accuracy for several datasets.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133757922","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

Operational theater generation by a descriptive language 通过描述性语言生成作战战区

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.19

Matis Ghiotto, B. Desbenoit, Romain Raffin

{"title":"Operational theater generation by a descriptive language","authors":"Matis Ghiotto, B. Desbenoit, Romain Raffin","doi":"10.24132/csrn.3301.19","DOIUrl":"https://doi.org/10.24132/csrn.3301.19","url":null,"abstract":"3D landscapes generation is an interdisciplinary field that requires expertise in both computer graphics and geographic informations systems (GIS). It is a complex and time-consuming process. In this paper, we present a new approach to simplify 3D environment generation process, by creating a go-between data-model containing a list of available source data and steps to use them. To feed the data-model, we introduce a formal language that describes the process\"s sequence. We propose an adapted format, designed to be human-readable and machine-readable, allowing for easy creation and modification of the scenery. We demonstrate the utility of our approach by implementing a prototype system to generate 3D landscapes with a use-case fit for multipurpose simulation. Our system takes a description as input and outputs a complete 3D environment, including terrain and feature elements such as buildings created by chosen geometrical process. Experiments show that our approach reduces the time and effort required to generate a 3D environment, making it accessible to a wider range of users without extensive knowledge of GIS. In conclusion, our custom language and implementation provide a simple and effective solution to the complexity of 3D terrain generation, making it a valuable tool for users in the area.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131455334","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

Polychromatism of all light waves: new approach to the analysis of the physical and perceptive color aspects 所有光波的多色性:分析物理和感知色彩方面的新方法

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.43

Justyna Niewiadomska-Kaplar

{"title":"Polychromatism of all light waves: new approach to the analysis of the physical and perceptive color aspects","authors":"Justyna Niewiadomska-Kaplar","doi":"10.24132/csrn.3301.43","DOIUrl":"https://doi.org/10.24132/csrn.3301.43","url":null,"abstract":"Research on light vision mechanisms in biosystems and on the mechanisms of formation of deficits in color discrimination[1] reveals that not only white light is polychromatic but all light waves are. The spectrum of white light is composed of aggregations of only 4 monochromatic waves: magenta UV 384 nm, cyan 432 nm, yellow 576 nm and magenta IR 768 nm, grouped in 5 bi-chromatic waves: cinnabar red (magenta IR + yellow), green (yellow + cyan), indigo (cyan + magenta UV) and also two semi-bright bi-chromatic waves - porphyry IR (semi-infrared wave composed of the magenta IR 768 nm wave and the colorless infrared wave 864 nm) and porphyry UV (semi-ultraviolet wave composed of the magenta UV 384 nm wave and the colorless ultraviolet wave 288 nm). The light waves thus composed create the light sensations due to the mechanism of additive synthesis. The method allows a new approach to interpret the composition of the bright waves, the phenomenon of decomposition of colours and additive synthesis that constitutes the principle of colour production in computers. The new elaborate models of colour physics also constitute the basis by interpretation of the mechanisms of vision of colours.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"57 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385467","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

Fast Incremental Image Reconstruction with CNN-enhanced Poisson Interpolation 基于cnn增强泊松插值的快速增量图像重建

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.24

Blaž Erzar, Žiga Lesar, Matija Marolt

引用次数: 0

Detection of Dangerous Situations Near Pedestrian Crossings using In-Car Camera 利用车载摄像头检测人行横道附近的危险情况

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.41

M. Kubanek, Lukasz Karbowiak, J. Bobulski

引用次数: 0