Impact Score 5.94
The emergence of artificial intelligence (AI) opens the way to new development potential for our industries and businesses. More and more, companies are using image recognition, to improve their processes and increase their productivity. Image recognition is a subclass of computer vision and artificial intelligence. It represents a set of methods to detect and analyze images to realize the automation of specific tasks. It is a technology that can identify places, people, objects, and many other types of elements in the image and draw conclusions through analysis. Depending on the type of information or concept required, photo or video recognition can be performed with different accuracy. In fact, models or algorithms can detect specific elements, just as they can simply assign images to a large category.
3D understanding has been attracting increasing attention of computer vision and graphics researchers recently. It is particularly relevant due to its importance for many applications such as self-driving cars, autonomous robots, virtual reality, and augmented reality. Behind the wide spectrum of applications lies the fundamental techniques in analyzing 3D data. Different from 2D images that have a dominant representation as pixel arrays, 3D data possesses multiple popular representations, such as point cloud, mesh, volumetric field, multi-view images and parametric models, each fitting their own application scenarios. From the research perspective, each type of data format has its own properties that pose challenges to deep architecture design while also provide the opportunity for novel and efficient solutions. In addition, fuzzy logic is an extension of traditional set theory which deals with the concept of partial truth value. Due to the uncertainty of data, task and result, partial truth values bring many difficulties to image processing. However, this uncertainty is not always due to randomness, but due to the inherent fuzziness of image data.
In addition to the randomness that probability theory can deal with, other defects in image processing include gray fuzziness, geometric fuzziness and fuzzy knowledge of image features. Fuzzy logic is a relatively young theory. The main advantage of this theory is that it allows the natural description of the problem to be solved in linguistic terms, rather than the relationship between precise values. This simplicity in dealing with complex systems is the main reason why fuzzy logic theory is widely used in image processing. Remote sensing images and other digital images can also be classified to clearly represent certain land cover categories in the resulting images.
The aim of this special section is to present an overview of the current status of the smart image recognition based on different state-of-the-art artificial intelligence techniques such as computer vision technology, 3D deep learning, and fuzzy logic theory.
Suggested topics include: Smart image recognition based on computer vision technology, fuzzy logic theory, and 3D deep learning; image classification based on AI; Location and detection of images based on AI; Segmentation task of images based on AI; Registration of images based on AI; Fusion of images based on AI.
New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.
Before submission, authors should carefully read the Guide for Authors available at
Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-sira” under the “Issues” tab.
For additional questions, contact the Main Guest Editor.
Submission of manuscript: Jan. 1, 2022
First notification: Feb. 1, 2022
Submission of revised manuscript: Mar. 1, 2022
Notification of the re-review: Apr. 1, 2022
Final notification: May 1, 2022
Final paper due: June 1, 2022
Publication: October 2022
Kelvin K.L. Wong (Managing Guest Editor)
School of Electrical and Electronics Engineering, University of Adelaide, Australia
E-mail: [email protected]
Kelvin K.L. Wong has more than 10 years of artificial intelligence and image processing research experience. He obtained a B.Eng. (Hons, 2001) in Mechanical and Production Engineering from Nanyang Technological University, a M.AIT. (2003) in Applied Information Technology from The University of Sydney, followed by a Ph.D. in Electrical and Electronic Engineering (2009) from The University of Adelaide. From 2006 to 2009, he was doing research work on medical imaging and cardiac flow analysis. Since 2009 to now, he was involved in computational fluid and solid dynamics. In 2013, he began working on discrete element method with applications in nuclear reactor design, first at Tsinghua University, and later in biological systems at The University of Western Australia.
He was the originator of the spatial game moment concept, in which multi-objective multi-constraint combinatorial optimization problems can be treated as decision-making problems in the game theoretical sense and solved with high efficiency. In addition, he is the first author of the book "Methods in Research and Development of Biomedical Devices", and a co-author of a second book "Computational Hemodynamics – Theory, Modelling and Applications". He has served as associate editors and as guest editors for journals in the area of biomedical engineering. Dr. Kelvin Wong is currently adjunct lecturer at the School of Electrical and Electronic Engineering, The University of Adelaide. His publications now span a diverse range of topics in the medical science and engineering field.
Simon James Fong
Department of Computer and Information Science, University of Macau, Macau
E-mail: [email protected]
Simon James Fong graduated from La Trobe University, Australia, with a 1st Class Honours B.Eng. drgree in Computer Systems and a Ph.D. degree in Computer Science in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 445 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. And published an article in “IEEE Journal of Selected Areas in Communication” with Impact Factor of 11.420, and ranked as the number 4 journal by Web of Science in the fields of Engineering, Electrical and Electronics. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier (I.F. 3.5), IEEE IT Professional Magazine, (I.F. 1.661) and various special issues of SCIE-indexed journals. Simon is also an active researcher with leading positions such as Vice-chair of IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and Vice-director of International Consortium for Optimization and Modelling in Science and Industry (iCOMSI).
Dhanjoo N. Ghista
University 2020 Foundation, Northborough, MA, USA.
E-mail: [email protected]
Dhanjoo N. Ghista has an international standing in education and research, having set up programs and departments at universities in responsible academic-administrative positions (from Department Head to CAO/Provost), and has been involved in planning universities. He has a multi-disciplinary background, spanning science and engineering, medicine and health sciences, social sciences, management, governance and public administration, and STEM education. He is a pioneer in the fields of biomedical engineering, computational medicine, healthcare engineering and management, governance and economy, and urban-rural sustainable community development, and is committed to the advancement of rural communities and developing countries. Dr. Ghista has a sterling scholarly record of having published 500+ papers and 30 books. His research involvements and publications have been in science and engineering, biomedical engineering, medicine, cognitive science and therapy, social sciences, sports science, education, sustainable communities, and the role of university in society. His books have been in biomedical engineering and physics, physiological mechanics, cardiovascular physics and engineering, orthopedic biomechanics, medical and life physics, spinal injury biomedical engineering, socio-economic democracy and world government. He has served on national research grants review panels, and has been editor of journals and book series (with publishers). Throughout his academic career, he has obtained substantial research grants, served on national agencies for research promotion, and contributed to community development.