International Conference on Quality Control by Artificial Vision最新文献_第9页

Interest of pseudo-focused images for key-points detection in plenoptic imaging 全光学成像中伪聚焦图像关键点检测的研究

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588954

M. Al Assaad, S. Bazeille, T. Laurain, A. Dieterlen, C. Cudel

引用次数: 3

Deflectometry based on light-field imaging 基于光场成像的偏转测量

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588988

A. Meguenani, K. Tout, S. Kohler, S. Bazeille, J. Chambard, C. Cudel

引用次数: 1

Out-of-distribution detection for fungi images with similar features 相似特征真菌图像的分布外检测

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2591725

Yutaka Kawashima, Mayuka Higo, T. Tokiwa, Yukihiro Asami, K. Nonaka, Y. Aoki

引用次数: 0

Improved visual saliency estimation on manufactured surfaces using high-dynamic reflectance transformation imaging 利用高动态反射变换成像改进了对加工表面的视觉显著性估计

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589748

M. Nurit, G. L. Le Goïc, S. Maniglier, P. Jochum, H. Chatoux, A. Mansouri

引用次数: 2

On learning deep domain-invariant features from 2D synthetic images for industrial visual inspection 面向工业视觉检测的二维合成图像深度域不变特征学习

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589040

Abdelrahman G. Abubakr, Igor Jovančević, Nour Islam Mokhtari, Hamdi Ben Abdallah, J. Orteu

{"title":"On learning deep domain-invariant features from 2D synthetic images for industrial visual inspection","authors":"Abdelrahman G. Abubakr, Igor Jovančević, Nour Islam Mokhtari, Hamdi Ben Abdallah, J. Orteu","doi":"10.1117/12.2589040","DOIUrl":"https://doi.org/10.1117/12.2589040","url":null,"abstract":"Deep learning resulted in a huge advancement in computer vision. However, deep models require a large amount of manually annotated data, which is not easy to obtain, especially in a context of sensitive industries. Rendering of Computer Aided Design (CAD) models to generate synthetic training data could be an attractive workaround. This paper focuses on using Deep Convolutional Neural Networks (DCNN) for automatic industrial inspection of mechanical assemblies, where training images are limited and hard to collect. The ultimate goal of this work is to obtain a DCNN classification model trained on synthetic renders, and deploy it to verify the presence of target objects in never-seen-before real images collected by RGB cameras. Two approaches are adopted to close the domain gap between synthetic and real images. First, Domain Randomization technique is applied to generate synthetic data for training. Second, a novel approach is proposed to learn better features representations by means of self-supervision: we used an Augmented Auto-Encoder (AAE) and achieved results competitive to our baseline model trained on real images. In addition, this approach outperformed baseline results when the problem was simplified to binary classification for each object individually.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"46 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":"124770077","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

Identification of insect infiltration time by tissue slice 组织切片法鉴定昆虫浸润时间

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588838

Y. Hashiguchi, K. Terada, K. Shinozaki, K. Miyama

引用次数: 0

Vision-based classification of mosquito species: data augmentation by background replacement for convolutional neural network-based species classification of smashed mosquitoes 基于视觉的蚊种分类:基于背景替换的卷积神经网络蚊种分类数据增强

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589100

Ryousuke Tsubaki, Takumi Toyoda, Kota Yoshida, Akio Nakamura

{"title":"Vision-based classification of mosquito species: data augmentation by background replacement for convolutional neural network-based species classification of smashed mosquitoes","authors":"Ryousuke Tsubaki, Takumi Toyoda, Kota Yoshida, Akio Nakamura","doi":"10.1117/12.2589100","DOIUrl":"https://doi.org/10.1117/12.2589100","url":null,"abstract":"This study proposes a method of data augmentation by background replacement for the species classification of smashed mosquitoes using convolutional neural networks (CNNs). To augment data from a limited number of images of smashed mosquitoes, varieties of foreground mosquito and background are ensured by clipping a foreground mosquito image and pasting it into different backgrounds. For the background images, a white image is prepared as the ideal background, and a hand palm image is assumed as the background for practical use. Images extracted from three publicly available datasets are also prepared, which are considered as the variable backgrounds. A CNN-based deep classification is used with three types of architecture, and the classification accuracy is compared using training images corresponding to different background conditions. The classification accuracy using training images with a variety of backgrounds is better than that with a white or palm background. Moreover, deep classification with a residual network achieves the highest classification accuracy. The results of this work show that the species classification of the smashed mosquitoes can be achieved by using datasets with the proposed data augmentation method.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"3 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":"115821522","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

Social distance measurement for indoor environments 用于室内环境的社交距离测量

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589105

E. Nadine-Erdene, S. Karungaru, K. Terada

引用次数: 0

Piano performance support system with a head-mounted display 配有头戴式显示器的钢琴演奏支持系统

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589062

Tomoyasu Kaji, K. Terada, S. Karungaru

引用次数: 0

Recipe recommendation and cooking instruction based on food material recognition 基于食材识别的食谱推荐和烹饪指导

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589081

Saki Asahina, Nobuyuki Umezu

{"title":"Recipe recommendation and cooking instruction based on food material recognition","authors":"Saki Asahina, Nobuyuki Umezu","doi":"10.1117/12.2589081","DOIUrl":"https://doi.org/10.1117/12.2589081","url":null,"abstract":"As devices such as depth cameras and projectors become cheaper, various methods have been proposed to make people's work environment smarter with AR technologies. In a kitchen, there would be a wide variety of advantages in an intelligent work environment, because people have to manage so many things such as various ingredients, typology of procedures with many recipes, and complicated work environment by mixing materials and tools. In this research, we aim to support cooking by recommending recipes with an installed projector and camera in the kitchen and projecting an operation interface. Our system recognizes ingredients with a DNN-based method called Mask R-CNN. It also has gesture controls that offer users contactless operations based on a Kinect depth sensor. We present four popular recipes obtained from a famous site named the Rakuten recipe. Displayed information includes dish names, ingredients, cooking procedures, process photographs, and time to cook. A series of user experiments with 10 participants was conducted to evaluate the usability of gesture operation with Kinect of the proposed system. The distance between Kinect and the hands of the participants is 0.8 (m). Each participant is given one trial and uses gestures to select one of four recipes displayed on a iMac screen. We received high evaluations (average 4.2 to 4.5 on a 5-point scale) in the results of the experiment questionnaire. Future work includes integrating more functions into our system, such as estimating ingredient amount based on the areas of recognized materials with Mask R-CNN, and cooking process recognition.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"62 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":"130567428","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