Computer Vision Techniques: Theory and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 9647
Special Issue Editors
Interests: fuzzy clustering; fuzzy set applications; pattern recognition; machine learning
Special Issue Information
Dear Colleagues,
Computer vision (CV) is a fast-growing domain of informatics concerned with making computers “see”—as in “understand”—imagery. Image acquisition is performed by image sensors that currently have capabilities which were unthinkable two decades ago. These sensors surpass the human eye as they can perceive things invisible to us but are limited in other aspects. Computers can be programmed to “understand” quite a lot from captured images, often things that humans do not see or care about. Still, computers are nowhere near the capability of the human brain. The gap is becoming smaller and smaller as technology and science advance. The applications of computer vision are wide and cover all aspects of social and economic activities: pattern recognition, nondestructive assessment of quality for various types of products, 3D object detection, agriculture, weather, healthcare, identification, surveillance, public safety, driving aides, self-driving vehicles, etc. Computers are already able to estimate defects and properties of products/objects such as color, shape, size, surfaces, contamination, and they can identify persons and track them. The limits to where computer vision can go cannot be established, but current developments suggest the limit is only given by our imagination and soon computers might even see better than us.
This Special Issue aims to provide a platform to publish recent original research, review papers or surveys in the state of the art of theoretical approaches and applications of computer vision.
Topics of interest to this Special Issue include but are not limited to:
- Image processing in CV;
- Feature detection and matching;
- Image segmentation;
- Image registration;
- Object recognition and tracking;
- Image classification and recognition;
- Motion analysis;
- Machine learning for CV;
- Biologically inspired CV;
- Applications.
Technical Program Committee Member:
Dr. Uscatu Cristian Răzvan: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest 010374, Romania
Prof. Dr. Miin-shen Yang
Prof. Dr. Cocianu Catalina-Lucia
Guest Editors
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Keywords
- image analysis
- image processing
- image understanding
- deep learning techniques for CV
- evolutionary computing and bio-inspired algorithms for CV
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