• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 32 matches

• 2025
• 
  Abdur Rahman, Jason Street, James Wooten, Mohammad Marufuzzaman, Veera Gnaneswar Gude, Randy K. Buchanan, Haifeng Wang:
  MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement. Expert Syst. Appl. 259: 125363 (2025)
• 2024
• 
  Jia Wang, Rui Ma, Wei Zhao, Hongjie Guo, Yuliang Yun, Li Li, Fengqi Hao, Jinqiang Bai, Dexin Ma:
  An Improved Non-Destructive and High-Efficiency Model for Detecting the Purity of Denghai 605 Maize Variety Based on Machine Vision and Deep Learning. IEEE Access 12: 141830-141840 (2024)
• 
  Victor Massaki Nakaguchi, R. M. Rasika D. Abeyrathna, Tofael Ahamed:
  Development of a new grading system for quail eggs using a deep learning-based machine vision system. Comput. Electron. Agric. 226: 109433 (2024)
• 
  Aro Kim, Seung-taek Woo, Minho Park, Dong-hwi Kim, Hanshin Lim, Soon-Heung Jung, Sangwoon Kwak, Sang-hyo Park:
  Deep learning-guided video compression for machine vision tasks. EURASIP J. Image Video Process. 2024(1): 32 (2024)
• 
  Stefano Toigo, Brendon Kasi, Daniele Fornasier, Angelo Cenedese:
  Flexible machine/deep learning microservice architecture for industrial vision-based quality control on a low-cost device. J. Electronic Imaging 33(3) (2024)
• 
  Wupeng Deng, Quan Liu, Feifan Zhao, Duc Truong Pham, Jiwei Hu, Yongjing Wang, Zude Zhou:
  Learning by doing: A dual-loop implementation architecture of deep active learning and human-machine collaboration for smart robot vision. Robotics Comput. Integr. Manuf. 86: 102673 (2024)
• 
  Abdur Rahman, Jason Street, James Wooten, Mohammad Marufuzzaman, Veera Gnaneswar Gude, Randy K. Buchanan, Haifeng Wang:
  MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement. CoRR abs/2409.04920 (2024)
• 2023
• 
  Lidong Wang, Huixi Zhang, Yin Zhang, Keyong Hu, Kang An:
  A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course. IEEE Access 11: 32671-32681 (2023)
• 
  Zhenglin Yang, Yadian Zhao, Chao Xu:
  Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning. Sensors 23(12): 5655 (2023)
• 
  Tommi Aihkisalo, Anu Purhonen, Ilkka Niskanen:
  Deep Learning Machine Vision Solutions for Monitoring Safety Structures to Supplement Building Information Models. SSE 2023: 1-10
• 
  Yi Huang:
  Intelligent Machine Vision for Detection of Steel Surface Defects with Deep Learning. SmartIoT 2023: 326-327
• 2022
• 
  Zijue Chen:
  Development of A Deep Learning Based Machine Vision System for Occluded 2D Fruit Trees. Monash University, Australia, 2022
• 
  Daoliang Li, Ling Du:
  Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish. Artif. Intell. Rev. 55(5): 4077-4116 (2022)
• 
  Aaron Rasheed Rababaah, Alaa Musa Rababah:
  Intelligent machine vision model for building architectural style classification based on deep learning. Int. J. Comput. Appl. Technol. 70(1): 11-21 (2022)
• 
  Aaron Rasheed Rababaah:
  Deep learning solution for machine vision problem of vehicle body damage classification. Int. J. Comput. Vis. Robotics 12(4): 426-442 (2022)
• 
  Zhenglong Ding, Wan Song, Shu Zhan:
  A Measurement System for the Tightness of Sealed Vessels Based on Machine Vision Using Deep Learning Algorithm. IEEE Trans. Instrum. Meas. 71: 1-15 (2022)
• 2021
• 
  Achref Elouni:
  Apprentissage profond et IA pour l'amélioration de la robustesse des techniques de localisation par vision artificielle. (Deep learning and AI for improving the robustness of machine vision localization techniques). University of Clermont Auvergne, Clermont-Ferrand, France, 2021
• 
  Lucas Costa, Yiannis Ampatzidis, Charles Rohla, Niels Maness, Becky Cheary, Lu Zhang:
  Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Comput. Electron. Agric. 181: 105964 (2021)
• 
  Keling Tu, Shaozhe Wen, Ying Cheng, Tingting Zhang, Tong Pan, Jie Wang, Jianhua Wang, Qun Sun:
  A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968′ using machine vision combined with deep learning. Comput. Electron. Agric. 182: 106002 (2021)
• 
  Bo Xiao, Shih-Chung Kang:
  Vision-Based Method Integrating Deep Learning Detection for Tracking Multiple Construction Machines. J. Comput. Civ. Eng. 35(2) (2021)
• 
  Handing Xu, Zhenguo Nie, Xinyu Cui, ShiKeat Lee, Qizhi Meng, Fugui Xie, Xin-Jun Liu:
  An Online Intelligent Kinematic Calibration Method for Quadruped Robots Based on Machine Vision and Deep Learning. ICIRA (4) 2021: 338-344
• 
  Kit Guan Lim, Daniel Siruno, Min Keng Tan, Chung Fan Liau, Sha Huang, Kenneth Tze Kin Teo:
  Mobile Machine Vision for Railway Surveillance System using Deep Learning Algorithm. IICAIET 2021: 1-6
• 
  Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos N. Plataniotis:
  Deep Learning and Machine Vision for Food Processing: A Survey. CoRR abs/2103.16106 (2021)
• 
  Abdul Mueed Hafiz, Shabir Ahmad Parah, Rouf Ul Alam Bhat:
  Attention mechanisms and deep learning for machine vision: A survey of the state of the art. CoRR abs/2106.07550 (2021)
• 2020
• 
  Xiankun Lin, Xin Wang, Li Li:
  Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model. Appl. Intell. 50(7): 2105-2119 (2020)
• 
  Alberto Tellaeche Iglesias, Iker Pastor-López, Borja Sanz-Urquijo, Pablo García Bringas:
  A Real Time Vision System Based on Deep Learning for Gesture Based Human Machine Interaction. HAIS 2020: 561-572
• 2019
• 
  Abozar Nasirahmadi, Barbara Sturm, Sandra A. Edwards, Knut-Håkan Jeppsson, Anne-Charlotte Olsson, Simone Müller, Oliver Hensel:
  Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs. Sensors 19(17): 3738 (2019)
• 2017
• 
  Joe Lemley, Shabab Bazrafkan, Peter Corcoran:
  Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electron. Mag. 6(2): 48-56 (2017)
• 
  Richard Wu, Ying Zhao, Alan Clarke, Anthony Kendall:
  A Framework Using Machine Vision and Deep Reinforcement Learning for Self-Learning Moving Objects in a Virtual Environment. AAAI Fall Symposia 2017: 231-237
• 
  Aswath Suresh, Chetan Arora, Debrup Laha, Dhruv Gaba, Siddhant Bhambri:
  Intelligent Smart Glass for Visually Impaired Using Deep Learning Machine Vision Techniques and Robot Operating System (ROS). RiTA 2017: 99-112

skipping 2 more matches