International Conference on Quality Control by Artificial Vision最新文献_第10页

Webcam-based categorization of task engagement of PC users at work 基于网络摄像头的PC用户工作任务参与分类

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589104

T. Ohara, Nobuyuki Umezu

{"title":"Webcam-based categorization of task engagement of PC users at work","authors":"T. Ohara, Nobuyuki Umezu","doi":"10.1117/12.2589104","DOIUrl":"https://doi.org/10.1117/12.2589104","url":null,"abstract":"In this paper, we propose a support method for PC users to monitor their task engagement. Our approach is based on the number of keyboard strokes and pixel changes on the screen, and images from an ordinary webcam equipped with a PC. With our system, supervisors or teachers would improve their quality of guidance and instructions given at the right moment when workers or students require some support due to reasons such as slow progress and technical difficulties. In conventional methods, a special device such as an acceleration sensor for each individual is often required to acquire information on one’s working status and body movements, which is difficult to deploy in a real environment due to its cost for sensors. A face detection method based on Deep Neural Network, such as SSD, allows as to implement a cheaper system using an ordinary web camera. We calculate the average difference between two grabbed frames from the user’s screen to estimate the amount of screen changes between a given time interval. The number of key strokes typed by the user is another factor to estimate their task engagement. These factors are used to categorize the work mode of users. We use the K-Means method based on the Euclidean distance to cluster the recorded factors to determine thresholds for task categorization. We conducted experiments seven participants to evaluate the accuracy of our categorization method. Every participant is asked to categorize 15 scenes into four work modes. A scene includes a camera image with the PC user’s face, the screenshot path the moment, and the number of key strokes. The results from these participants are then compared with those of our system that categorized the same scenes with the thresholds on three factors. Approximately only 60% of these results matched each other, where we have enough room to improve our approach. Future work includes the selection of features that are far more effective for categorization, a better estimation of pixel changes on the PC screen, and evaluation experiments with more participants.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134288356","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

Inspection of mechanical assemblies based on 3D deep learning approaches 基于3D深度学习方法的机械装配检测

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588986

Assya Boughrara, Igor Jovančević, Hamdi Ben Abdallah, B. Dolives, Mathieu Belloc, J. Orteu

引用次数: 3

You don't drink a cupboard: improving egocentric action recognition with co-occurrence of verbs and nouns 你不喝橱柜:通过动词和名词共现来提高自我中心行为识别

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2591298

Hiroki Kojima, Naoshi Kaneko, Seiya Ito, K. Sumi

引用次数: 0

Development of image simulator for forward-looking sonar using 3D rendering 基于三维渲染的前视声纳图像模拟器的开发

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2590004

Dingyu Liu, Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya, A. Yamashita, H. Asama

{"title":"Development of image simulator for forward-looking sonar using 3D rendering","authors":"Dingyu Liu, Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya, A. Yamashita, H. Asama","doi":"10.1117/12.2590004","DOIUrl":"https://doi.org/10.1117/12.2590004","url":null,"abstract":"This paper proposes an efficient imaging sonar simulation method based on 3D modeling. In underwater scenarios, a forward-looking sonar, which is also known as an acoustic camera, outperforms other sensors including popular optical cameras, for it is resistant to turbidity and weak illumination, which are typical in underwater environments, and thus able to provide accurate information of the environments. For those underwater tasks highly automated along with artificial intelligence and computer vision, the development of the acoustic image simulator can provide support by reproducing the environment and generating synthetic acoustic images. It can also facilitate researchers to tackle the scarcity of real underwater data in some theoretical studies. In this paper, we make use of the 3D modeling technique to simulate the underwater scenarios and the flexible automated control of the acoustic camera and objects in the scenarios. The simulation results and the comparison to real acoustic images demonstrate that the proposed simulator can generate accurate synthetic acoustic images efficiently and flexibly.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132158935","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

Verification of denoising performance of edge-preserving noise reduction filter using fast M-estimation method 用快速m估计方法验证保边降噪滤波器的去噪性能

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2592763

Y. Kondo, Y. Yamaguchi, H. Saito, I. Yoshida, M. Numada, H. Koshimizu

引用次数: 0

Active learning using weakly supervised signals for quality inspection 基于弱监督信号的质量检测主动学习

International Conference on Quality Control by Artificial Vision Pub Date : 2021-04-07 DOI: 10.1117/12.2586595

Antoine Cordier, Deepan Das, Pierre Gutierrez

引用次数: 7

Synthetic training data generation for deep learning based quality inspection 基于深度学习的质量检测综合训练数据生成

International Conference on Quality Control by Artificial Vision Pub Date : 2021-04-07 DOI: 10.1117/12.2586824

Pierre Gutierrez, Maria Luschkova, Antoine Cordier, Mustafa Shukor, Mona Schappert, Tim Dahmen

{"title":"Synthetic training data generation for deep learning based quality inspection","authors":"Pierre Gutierrez, Maria Luschkova, Antoine Cordier, Mustafa Shukor, Mona Schappert, Tim Dahmen","doi":"10.1117/12.2586824","DOIUrl":"https://doi.org/10.1117/12.2586824","url":null,"abstract":"Deep learning is now the gold standard in computer vision-based quality inspection systems. In order to detect defects, supervised learning is often utilized, but necessitates a large amount of annotated images, which can be costly: collecting, cleaning, and annotating the data is tedious and limits the speed at which a system can be deployed as everything the system must detect needs to be observed first. This can impede the inspection of rare defects, since very few samples can be collected by the manufacturer. In this work, we focus on simulations to solve this issue. We first present a generic simulation pipeline to render images of defective or healthy (non defective) parts. As metallic parts can be highly textured with small defects like holes, we design a texture scanning and generation method. We assess the quality of the generated images by training deep learning networks and by testing them on real data from a manufacturer. We demonstrate that we can achieve encouraging results on real defect detection using purely simulated data. Additionally, we are able to improve global performances by concatenating simulated and real data, showing that simulations can complement real images to boost performances. Lastly, using domain adaptation techniques helps improving slightly our final results.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121692138","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: 9

Visual inspection for metallic surfaces: CNN driven by features 金属表面的视觉检测:由特征驱动的CNN

International Conference on Quality Control by Artificial Vision Pub Date : 2019-07-16 DOI: 10.1117/12.2521455

Riccardo Fantinel, A. Cenedese

{"title":"Visual inspection for metallic surfaces: CNN driven by features","authors":"Riccardo Fantinel, A. Cenedese","doi":"10.1117/12.2521455","DOIUrl":"https://doi.org/10.1117/12.2521455","url":null,"abstract":"In this paper, an effective and novel automatic learning solution for the quality control of metallic objects surfaces is proposed, which can be seamlessly integrated into the industrial process. Such a system requires a coaxial illuminator to capture the object view with a single camera while lighting it with structured light: in this way, the object surface can be viewed in time as a dynamic scene under different illumination conditions. By relying on a linear model to describe the expected evolution of the light over the object of interest, the Residuals of Linear Evolution of Light (RLEL) algorithm is derived with the specific aim of identifying and characterizing anomalies and defects through the residuals of a least square approach. Then, a novel learning strategy is developed that exploits the model-based RLEL descriptor and thus promotes itself as an alternative strategy to the black box approach of Convolutional Neural Networks (CNNs). By combining both the data-driven and the model-based learning approaches to perform the inspection task, an Hybrid Learning (HL) procedure is defined: in a first phase, the HL exploits an Encoder-Decoder network to incorporate the model-based description while, in a second phase, it uses only the pre-trained encoder to drive the learning process of a 3D-CNN. In doing so, the proposed procedure reaches interesting results that exceed also the performance of state-of-the-art 3D-Inception and 3D-Residual networks.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127460136","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

Automated vision system for crankshaft inspection using deep learning approaches 使用深度学习方法的曲轴检测自动视觉系统

International Conference on Quality Control by Artificial Vision Pub Date : 2019-07-16 DOI: 10.1117/12.2521751

K. Tout, Mohamed Bouabdellah, C. Cudel, J. Urban

引用次数: 2

A simplified approach for tomographic diffractive microscopy 层析衍射显微镜的一种简化方法

International Conference on Quality Control by Artificial Vision Pub Date : 2019-07-16 DOI: 10.1117/12.2521798

L. Foucault, N. Verrier, M. Debailleul, B. Simon, O. Haeberlé

引用次数: 0