Journal of Imaging Science and Technology最新文献

Development of Paper Temperature Prediction Method in Electrophotographic Processes by Using Machine Learning and Thermal Network Model 利用机器学习和热网络模型开发电子照相工艺中的纸张温度预测方法

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2024-01-01 DOI: 10.2352/j.imagingsci.technol.2024.68.1.010504

Takamasa Hase, Takumi Ishikura, Shinichi Kuramoto, Koichi Kato, K. Fushinobu

引用次数: 0

Detection Performance of X-ray Cascaded Talbot–Lau Interferometers Using W-absorption Gratings 使用 W-absorption 光栅的 X 射线级联塔尔博特-劳干涉仪的探测性能

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2024-01-01 DOI: 10.2352/j.imagingsci.technol.2024.68.1.010505

Jianheng Huang, Jiacheng Zeng, Minghui Zhu, Chengming Feng, Y. Lei, Xin Liu, Ji Li

引用次数: 0

Salient Semantic-SIFT for Robot Visual SLAM Closed-loop Detection 用于机器人视觉 SLAM 闭环检测的显著语义-SIFT

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2024-01-01 DOI: 10.2352/j.imagingsci.technol.2024.68.1.010502

Lihe Hu, Yi Zhang, Yang Wang

引用次数: 0

Camera Motion Estimation Method using Depth-Normalized Criterion 使用深度归一化准则的摄像机运动估算方法

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060403

Seok Lee

引用次数: 0

New Perspective on Progressive GANs Distillation for One-class Anomaly Detection 一类异常检测中GANs精馏的新进展

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060504

Yu-Feng Dong, Zhao Zhang, Hanyu Peng, Shifeng Chen

引用次数: 0

Daytime and Nighttime Image Haze Removal based on Improved Dark Channel Prior, Multiple-Scale Retinex, and Sparrow Search Optimization 基于改进的暗通道先验、多尺度Retinex和稀疏搜索优化的昼夜图像雾度去除

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060503

Chuang Li, Hao Zhou, Xin Xie, Yuanyuan Liu, Xiaoran Wang, Guangyu Lu, Hailing Xiong

引用次数: 0

Performance Boosting Mislabels Correction with Semi-Supervised Learning and Deep Feature Similarity Measurements 基于半监督学习和深度特征相似度测量的性能提升错误标记校正

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060501

Chi-Chia Sun, Jing. Guo, Jheng-Han Lin, Ting-Yu Chang

引用次数: 0

Color Image Stitching Elimination Method based on Co-occurrence Matrix 基于共生矩阵的彩色图像拼接消除方法

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060502

Y. Su

引用次数: 0

Development and Implementation of an Augmented Reality Thunderstorm Simulation for General Aviation Weather Theory Training 开发和实施用于通用航空气象理论培训的增强现实雷暴模拟器

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060402

Kexin Wang, Jack Miller, Philippe Meister, Michael C. Dorneich, Lori Brown, Geoff Whitehurst, E. Winer

{"title":"Development and Implementation of an Augmented Reality Thunderstorm Simulation for General Aviation Weather Theory Training","authors":"Kexin Wang, Jack Miller, Philippe Meister, Michael C. Dorneich, Lori Brown, Geoff Whitehurst, E. Winer","doi":"10.2352/j.imagingsci.technol.2023.67.6.060402","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2023.67.6.060402","url":null,"abstract":". In 2021, there were 1,157 general aviation (GA) accidents, 210 of them fatal, making GA the deadliest civil aviation category. Research shows that accidents are partially caused by ineffective weather theory training. Current weather training in classrooms relies on 2D materials that students often ﬁnd difﬁcult to map into a real 3D environment. To address these issues, Augmented Reality (AR) was utilized to provide 3D immersive content while running on commodity devices. However, mobile devices have limitations in rendering, camera tracking, and screen size. These limitations make the implementation of mobile device based AR especially challenging for complex visualization of weather phenomena. This paper presents research on how to address the technical challenges of developing and implementing a complex thunderstorm visualization in a marker-based mobile AR application. The development of the system and a technological evaluation of the application’s rendering and tracking performance across different devices is presented.","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Modeling on Large Kernel Metamaterial Neural Network. 大内核超材料神经网络的数字建模

IF 1 4区计算机科学

Journal of Imaging Science and Technology Pub Date : 2023-11-01 DOI: 10.2352/j.imagingsci.technol.2023.67.6.060404

Quan Liu, Hanyu Zheng, Brandon T Swartz, Ho Hin Lee, Zuhayr Asad, Ivan Kravchenko, Jason G Valentine, Yuankai Huo

{"title":"Digital Modeling on Large Kernel Metamaterial Neural Network.","authors":"Quan Liu, Hanyu Zheng, Brandon T Swartz, Ho Hin Lee, Zuhayr Asad, Ivan Kravchenko, Jason G Valentine, Yuankai Huo","doi":"10.2352/j.imagingsci.technol.2023.67.6.060404","DOIUrl":"10.2352/j.imagingsci.technol.2023.67.6.060404","url":null,"abstract":"<p><p>Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.</p>","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"67 6","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10970463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140305875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0