Submission Deadline: 31 March 2025 View: 289 Submit to Special Issue
Prof. Kechen Song
Email: songkc@me.neu.edu.cn
Affiliation: School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China.
Homepage: http://faculty.neu.edu.cn/songkc/en
Research Interests: vision-based inspection system for steel surface defects, surface topography, image processing, pattern recognition, and robotics
Prof. Shaopeng Hu
Email: hsp@hiroshima-u.ac.jp
Affiliation: Digital Manufacturing Education and Research Center, Hiroshima University, Hiroshima, 7398527, Japan.
Homepage: http://www.robotics.hiroshima-u.ac.jp
Research Interests: high-speed vision, stereo measurement, smart inspection and monitoring
Dr. Xin Wen
Email: wen_xin@sut.edu.cn
Affiliation: School of Software, Shenyang University of Technology, Shenyang 110870, C, Shenyang, 110819, China.
Homepage: https://scholar.google.co.uk/citations?hl=en&user=WZLo0E4AAAAJ
Research Interests: machine vision and surface topography
Machine vision detection and intelligent recognition are important research areas in computer vision with wide-ranging applications in manufacturing, healthcare, security, transportation, robotics, industrial production, aerospace, and many other industries. Machine vision detection involves capturing visual information using cameras or sensors and then using techniques such as image processing and machine learning to extract relevant features or identify defects in manufacturing processes. Intelligent recognition involves using algorithms to identify and classify objects or patterns in images or videos, enabling automated decision-making in various applications.
Recent advancements in these areas have been driven by the acceleration of algorithms such as image processing and deep learning, as well as hardware such as CPUs and GPUs. This has made machine vision detection and recognition more accurate and efficient, particularly in areas such as defect detection, industrial monitoring, high-speed target detection, 3D measurement, intelligent recognition, and so on.
However, to make machine vision detection and intelligent recognition technologies more reliable and effective in practical applications, several challenges still need to be addressed. These include how to process massive amounts of image or video data, improve real-time processing, ensure data privacy and security (e.g., in healthcare and surveillance), increase detection and recognition accuracy in complex environments, and enhance the interpretability of deep learning algorithms. Addressing these challenges will be crucial to enable the widespread adoption of machine vision detection and intelligent recognition technologies in various applications.
Overall, machine vision detection and intelligent recognition have the potential to revolutionize many industries and are an active area of research in computer vision. This special issue provides a platform for researchers and practitioners to share their latest findings and insights in machine vision detection and intelligent recognition. The special issue welcomes original research articles and review articles that report on the latest advancements and challenges in this field. The topics of interest for this special issue include, but are not limited to:
· Object detection and tracking
· Deep learning and pattern recognition
· Defect detection and segmentation
· 3D measurement and reconstruction
· Surveillance and security using machine vision
· Human-computer interaction
· Visual inspection and monitoring
· High-speed vision
· Image segmentation and classification
· Real-time image processing
· Remote monitoring and control of industrial processes
· Multiple camera or sensor systems
· Scene understanding and activity recognition
· Autonomous driving and obstacle detection
· Simultaneous localization and mapping
· Visual servoing and control of robots
· Evaluation and benchmarking of algorithm or system
· Challenges and future directions