Machine Vision and Augmented Intelligence

This book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing.

Highlights recent research in machine vision and augmented intelligence Discusses the outcomes of MAI 2022 Serves as a reference guide for researchers and practitioners in academia and industry

Autorentext
Koushlendra Kumar Singh is currently working as an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology, Jamshedpur, India. He completed his doctoral degree and master's program at the Indian Institute of Information Technology, Design, and Manufacturing, Jabalpur, India, in 2016 and 2011, respectively. Dr. Singh graduated in computer science and engineering from Bhagalpur College of Engineering, Bhagalpur, in 2008. He has published several papers in international refereed journals and conferences. His current research interest areas are image processing, biometrics, different applications of fractional derivatives, computational modeling, epidemic forecasting, etc.
Manish Kumar Bajpai is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Information Technology Design and Manufacturing, Jabalpur, India. He completed his Ph.D. from IIT Kanpur in image reconstruction and parallel algorithm design. Dr. Bajpai has published over 50 publications in international journals and conferences. He has several sponsored research and consultancy projects funded by agencies such as SPARC, MHRD, DST, USIEF, ATAL, BRNS, and NVIDIA. Eleven students have completed/pursuing their Ph.D. under his supervision. His research areas are augmented intelligence, machine vision, brain-computer interface, medical imaging, and parallel algorithms design. Dr. Bajpai is a senior member of IEEE and a life member of the Indian Science Congress and Indian Nuclear Society.
Dr. Akbar Sheikh Akbari is an associate professor (Reader) of Electronic Engineering in the School of Built Environment, Engineering and Computing. He has a B.Sc. (Hons), M.Sc. (distinction), and Ph.D. in Electronic and Electrical Engineering. After completing his Ph.D. at Strathclyde University, he joined Bristol University to work on an EPSRC project in stereo/multi-viewvideo processing. Dr. Sheikh Akbari's main areas of research are: biometric identification techniques, e.g., iris, ear, and face recognition, image resolution enhancement (super-resolution) methods, image source camera identification, color constancy (white balancing) techniques, standard and non-standard image/video codecs, e.g., H.264 and HEVC, multi-view image/video processing, video analytics and real-time embedded systems, edge detection in low SNR environments, compressive sensing, retro-re ective materials, online camera calibration. Dr. Sheikh Akbari was the academic supervisor for the following KTP project: Omega Security Systems/Leeds Beckett University, "Research, develop and implement a scalable and modular system which monitors and analyses individual behavioural patterns and movements in a range of environments", Funder: innovate UK, 24 months, £122,040, which was graded outstanding by innovate UK.

Inhalt
Modelling of Disease Transformation.- Epidemic Forecast.- COVID-19: Theory and practice.- Image Processing and Computer Vision.- Augmented Intelligence: Theory and Applications.- Soft Computing: Theory and Applications.- Deep Learning: Theory and Applications.