Guest Editors
Prof. Dr. Kwang Baek Kim
Email: gbkim@silla.ac.kr
Affiliation: Department of Computer Engineering, Silla University, Busan 46958, South Korea
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Research Interests: deep neural networks, deep learning, fuzzy neural networks, fuzzy systems, medical image processing and analysis, computer vision
Prof. Dr. Doo Heon Song
Email: mypham@hanmail.net
Affiliation: Department of Computer Games, Yong-in Art & Science University, Gyeonggi-do 17092, South Korea
Homepage:
Research Interests: machine learning, medical image processing, fuzzy systems
Prof. Dr. Sejong Lee
Email: kingsaejong@yu.ac.kr
Affiliation: School of Computer Science and Engineering, Yeungnam University, Gyeongbuk 38541, South Korea
Homepage:
Research Interests: blockchain-based medical data-sharing systems, artificial intelligence, data mining, cloud platform
Summary
With recent advanced machine learning techniques, there have been significant achievements in the development of efficient image processing techniques in many practical fields such as medical diagnosis, remote sensing, machine/robot vision, and video processing, However, the effectiveness is usually adversely affected when the data is not well defined due to inherent noise, uncertainty, ambiguity, vagueness, and incompleteness.
Under such conditions, fuzzy logic techniques turn out to be helpful in increasing robustness in challenging real-world image processing problems. Developing efficient segmentation algorithms with robustness under noisy environments has been a challenge. Data clustering algorithms, both crisp and uncertainty-based based have been extensively used in image segmentation. Image restoration/recovery technique that removes noise from images and complements missing parts, has been developed to restore more natural and high-quality images.
Thus, the Special Issue will focus on the exploration of the practical impacts of fuzzy techniques, in the field of image processing and its applications. Especially, the issue is devoted to the development of uncertainty-based image processing algorithms using fuzzy sets, rough sets, intuitionistic fuzzy sets, and soft sets. It will also cover several applications of these algorithms in medical diagnosis, weather prediction, satellite image analysis, market research, pattern recognition, medicine, and business. Special emphasis will also be given to the development of image processing techniques such as image segmentation, image recovery/restoration, and image data clustering using uncertainty-based models. Prospective authors are invited to submit previously unpublished works in these areas.
• Fuzzy techniques in contrast enhancement, edge detection, noise detection and removal, segmentation, geometric measurement, image recovery/restoration, and clustering of image data
• Fuzzy techniques in Pattern recognition and scene description, modeling of image data, object matching, image annotation, and image retrieval
• Fuzzy techniques in real-world applications use image data, such as medical/biological image segmentation, face recognition, industrial product inspection, automated surveillance, etc.
• hybrid approaches of fuzzy machine learning and fuzzy deep learning for image processing
Keywords
image processing, fuzzy logic, uncertainty, robustness, machine learning, segmentation, pattern analysis, vision
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