This book constitutes peer-reviewed proceedings of satellite workshops of the 12th Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP 2021). The book focuses on medical image processing, digital heritage, document analysis and recognition, and computer vision applications. The first part includes submissions on digital archiving and restoration methods with interesting and innovative research components. The second part focuses on medical imaging modalities including MRI, X-ray, CT, imaging in nuclear medicine, medical ultrasound, optical and confocal microscopy, and video and range data images. The third part deals with document analysis and recognition and focuses on text recognition, document layout analysis, understanding, historical and degraded document analysis, datasets, performance evaluation, metrics, etc. The fourth part of this book includes research work from academia and industry across the globe on smart, innovative, and practical applications of computer vision for industrial and societal impact. This book shares innovative ideas, experience and expertise, and ongoing research ideas and will be helpful for researchers and practitioners in academia and industry.

Presents select proceedings of the satellite workshops of the 12th ICVGIP 2021 Discusses recent findings in document analysis and recognition Shares innovative ideas, experience, and expertise in computer vision applications

Autorentext

Uma Mudenagudi is Dean, R&D, and Professor of ECE department at KLE Technological University Hubballi. She completed her Ph.D. from the Department of Computer Science, IIT Delhi, and M.Tech. degree from IIT Bombay. Her research areas are computer vision, computer graphics, image, and video analysis. She has got over 70 projects to her credit.

Aditya Nigam received his Master and Doctoral degrees from the Indian Institute of Technology Kanpur in 2009 and 2014, respectively. Presently, he is an Assistant Professor at IIT Mandi in the School of Computing and Electrical Engineering (SCEE). His research areas are biometrics, image processing, computer vision, and machine learning. He has several papers published to his credit.

Ravi Kiran Sarvadevabhatla is an Assistant Professor at the International Institute of Information Technology, Hyderabad. His work is primarily in the area of computer vision and applied machine learning. He has broad-ranging research interests and likes to work on inter-disciplinary problems involving multi-modal multimedia data (e.g., images, videos, text, audio/speech, eye-tracking data) and disciplines (e.g., graphics, robotics, human-computer interaction). He has got over 20 papers published to his credit.

Ayesha Choudhary is an Assistant Professor at the School of Computer & Systems Sciences at Jawaharlal Nehru University, India. She completed her Ph.D. from the Department of Computer Science and Engineering at the Indian Institute of Technology, Delhi (IIT Delhi). Her areas of research are computer vision and machine learning and their applications in Intelligent Transportation Systems, Assisted Living and Smart Agriculture. She has publications in various international journals and conferences to her credit.

Inhalt
Systematic Approach to Tuning a Deep CNN Classifying Bharatanatyam Mudras.- Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites.- Heritage Representation of Kashi Vishweshwar Temple at Kalabgoor, Telangana with Augmented Reality Application Using Photogrammetry.- Augmented Data as an Auxiliary Plug-In Toward Categorization of Crowdsourced Heritage Data.- Evolution of Bagbazar Street Through Visibility Graph Analysis (17462020).