This book describes various new computer based approaches which can be exploited for the (digital) reconstruction, recognition, restoration, presentation and classification of digital heritage. They are based on applications of virtual reality, augmented reality and artificial intelligence, to be used for storing and retrieving of historical artifacts, digital reconstruction, or virtual viewing.
The book is divided into three sections: "Classification of Heritage Data" presents chapters covering various domains and aspects including text categorization, image retrieval and classification, and object spotting in historical documents. Next, in "Detection and Recognition of Digital Heritage Artifacts", techniques like neural networks or deep learning are used for the restoration of degraded heritage documents, Tamil Palm Leaf Characters recognition, the reconstruction of heritage images, and the selection of suitable images for 3D reconstruction and classification of Indian land mark heritage images. Lastly, "Applications of Modern Tools in Digital Heritage" highlights some example applications for dance transcription, architectural geometry of early temples by digital reconstruction, and computer vision based techniques for collecting and integrating knowledge on flora.
This book is mainly written for researchers and graduate students in digital preservation and heritage, or computer scientists looking for applications of virtual reality, computer vision, and artificial intelligence techniques.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
76
65 s/w Abbildungen, 76 farbige Abbildungen
XIV, 272 p. 141 illus., 76 illus. in color.
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-3-030-57909-8 (9783030579098)
DOI
10.1007/978-3-030-57907-4
Schweitzer Klassifikation
Jayanta Mukhopadhyay is professor in the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur. His research interests are in image processing, computer vision, robotics, pattern recognition, computer graphics, multimedia systems and bio-medical informatics, and has published about 250 research papers in journals and conference proceedings in these areas.
Indu Sreedevi is Dean (Student Welfare) and professor in the Electronics and Communication Engineering Department of Delhi Technological University. Her main areas of research interest are in computer vision, sensor networks and image processing. She has published around 150 papers in international journals and international conferences.
Bhabatosh Chanda is professor at the Image Processing Laboratory of the Indian Statistical Institute in Kolkata. His research interest includes Image and video processing, pattern recognition, computer vision and mathematical morphology. He has published more than 200 technical articles in refereed journals and conferences, authored two books and edited six books. During his 25-year career, he received several awards, among them the Young Scientist Medal of the Indian National Science Academy in 1989, and the Computer Engineering Division Medal of the Institution of Engineers in 1998.
Santanu Chaudhury is currently Director of IIT Jodhpur. He is also a Professor in the Department of Electrical Engineering at IIT Delhi. His main research interests are in the fields of computer vision and pattern recognition. He was awarded the INSA medal for young scientists in 1993, and he is a fellow of the Indian National Academy of Engineers, the National Academy of Sciences, India, and the International Association of Pattern Recognition. He has authored/edited four books and more than 250 research publications in peer reviewed journals and conferences.
Vinay P.Namboodiri is associate professor at the Computer Science and Engineering Department of IIT Kanpur. His research interests include computer vision and machine learning with a focus on deep learning based research. He has over 45 publications in leading journals and conferences in computer vision.
Part I: Classification and Retrieval of Heritage Data.- Introduction to Heritages and HeritageManagement: A Preview.- Language-Based Text Categorization: A Survey.- Classification of Yoga Asanas from a Single Image by Learning the3D View of Human Poses.- IHIRD: A Data Set for Indian Heritage Image Retrieval.- Object Spotting in Historical Documents.- Part II: Restoration and Reconstruction of Digital Heritage Artifacts.- Text Extraction and Restoration of Old Handwritten Documents.- Categorization and Selection of Crowdsourced Images Towards 3D Reconstruction of Heritage Sites.- Deep Learning-Based Filtering of Images for 3D Reconstruction of Heritage Sites.- Improving Landmark Recognition Using Saliency Detection and Feature Classification.- Part III: Applications ofModern Tools in Digital Heritage.- Bharatanatyam Dance Transcription Using Multimedia Ontology and Machine Learning.- Evolution and Interconnection: Geometry in Early Temple Architecture.- Computer Vision for Capturing Flora.
