Lecture Notes in Electrical Engineering 924 Uma Mudenagudi Aditya Nigam Ravi Kiran Sarvadevabhatla Ayesha Choudhary   Editors Proceedings of the Satellite Workshops of ICVGIP 2021 Lecture Notes in Electrical Engineering Volume 924 Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM-Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. 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This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Contents Workshop on Digital Heritage (WDH) Systematic Approach to Tuning a Deep CNN Classifying Bharatanatyam Mudras ............................................ 3 R. Jisha Raj, Smitha Dharan, and T. T. Sunil Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites .......................... 25 Sunil V. Gurlahosur, S. M. Meena, Uday Kulkarni, Winston Dcosta, Vineet Lokur, Rohan V. Sirigeri, Sajal Porwal, S. P. Sammed, and Uma Mudenagudi Heritage Representation of Kashi Vishweshwar Temple at Kalabgoor, Telangana with Augmented Reality Application Using Photogrammetry ............................................ 43 Tejas Pawar, Aman Sharma, and Shiva Ji Augmented Data as an Auxiliary Plug-In Toward Categorization of Crowdsourced Heritage Data ..................................... 53 Shashidhar Veerappa Kudari, Akshaykumar Gunari, Adarsh Jamadandi, Ramesh Ashok Tabib, and Uma Mudenagudi Evolution of Bagbazar Street Through Visibility Graph Analysis (1746–2020) ....................................................... 63 Shilpi Chakraborty and Shiva Ji Mapping Archaeological Remains of 14th Century Fort of Jahanpanah Using Geospatial Analysis ............................ 79 Gaurav Kumar Pal and M. B. Rajani Spatial Analysis and 3d Mapping Historic Landscapes—Implications of Adopting an Integrated Approach in Simulation and Visualization of Landscapes ............. 87 Mythrayi Harshavardhan v vi Contents Medical Image Processing (MedImage) HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification ...................................... 105 Aman Verma and Vibhav Prakash Singh Document Analysis and Recognition (DAR) Model Compression Based Lightweight Online Signature Verification Framework ............................................ 123 Chandra Sekhar Vorugunti, S. Balasubramanian, Pulabaigari Viswanath, and Avinash Gautam End-to-End Transformer-Based Architecture for Text Recognition from Document Images ............................................ 135 Dipankar Ganguly, Akkshita Trivedi, Bhupendra Kumar, Tushar Patnaik, and Santanu Chaudhury A Hybrid Approach for Table Detection in Document Images .......... 147 Sunil Kumar Vengalil, Kevin Xavier, Konda Amith Sai, Sree Harsha, Ganesh Barma, and Neelam Sinha Workshop on Computer Vision Applications (WCVA) The Ikshana Hypothesis of Human Scene Understanding .............. 161 Venkata Satya Sai Ajay Daliparthi Worst-Case Adversarial Perturbation and Effect of Feature Normalization on Max-Margin Multi-label Classifiers ................ 183 Ritesh Kumar Gupta and Yashaswi Verma Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL) .............................................. 199 Binoy Saha and Sukhendu Das MATIC: Memory-Guided Adaptive Transformer for Image Captioning ........................................................ 219 Gaurav O. Gajhiye and Abhijeet V. Nandedkar Semantic Map Injected GAN Training for Image-to-Image Translation ....................................................... 235 Balaram Singh Kshatriya, Shiv Ram Dubey, Himangshu Sarma, Kunal Chaudhary, Meva Ram Gurjar, Rahul Rai, and Sunny Manchanda TextGen3D: A Real-Time 3D-Mesh Generation with Intersecting Contours for Text .................................................. 251 Ankit Dhiman, Praveen Agrawal, Sourav Kumar Bose, and Basavaraja Shanthappa Vandrotti About the Editors 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. vii Workshop on Digital Heritage (WDH) Systematic Approach to Tuning a Deep CNN Classifying Bharatanatyam Mudras R. Jisha Raj , Smitha Dharan , and T. T. Sunil 1 Introduction Dance and music are an inevitable part of human culture across the world, and it reflects the lives, beliefs and cultural traditions of people practising it. Archaeologists have excavated sculptures and paintings of dancers across India [3, 15, 21]. Sangeet Nataka Academy has identified eight different classical dances in India [16]. Bharatanatyam is a classical dance that originated in South India and is performed by both men and women [30]. The performers of Bharatanatyam dance use hand gestures (mudras), facial expressions and body movements that are very graceful [24]. They use these media to communicate to the audience the intended meaning of the verse or shloka being sung with the accompaniment of traditional musical instruments. According to Natyashastra, a classical text on Indian dance, there are 28 Asamyukta Hastas (single hand gestures) and 23 Samyukta Hastas (Double hand gestures) [7, 9, 23] in Bharatanatyam. Computer vision techniques can be applied to Bharatanatyam mudras to obtain a better understanding and annotating a dance performance. Open datasets on Bharatanatyam hand gestures are not presently avail- able. An exhaustive Bharatanatyam Mudra dataset consisting of 15,396 static single hand gesture images and 13,035 static double hand gesture images was proposed by the authors and is available in public domain now. The full dataset can be down- loaded from https://github.com/jisharajr/Bharatanatyam-Mudra-Dataset.git. Using this dataset classification using conventional machine learning techniques, feature descriptors and visualisation was done and are being reported elsewhere. B R. Jisha Raj ( ) · S. Dharan · T. T. Sunil College of Engineering, Chengannur, Attingal, Kerala, India e-mail: [email protected] S. Dharan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 3 U. Mudenagudi et al. (eds.), Proceedings of the Satellite Workshops of ICVGIP 2021, Lecture Notes in Electrical Engineering 924, https://doi.org/10.1007/978-981-19-4136-8_1