Computer Vision and Robotics

Nyhet

Proceedings of CVR 2025, Volume 1

Häftad, Engelska, 2026

AvHarish Sharma,Abhishek Bhatt,Chirag Modi,Andries Engelbrecht

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This book consists of a collection of the high-quality research articles in the field of computer vision and robotics which are presented at the International Conference on Computer Vision and Robotics (CVR 2025), organized by National Institute of Technology, Goa, India, during 25–26 April 2025. The book discusses applications of computer vision and robotics in the fields like medical science, defence, and smart city planning. The book presents recent works from researchers, academicians, industry, and policy makers.

Produktinformation

Utgivningsdatum2026-01-03
Mått155 x 235 x 28 mm
Vikt774 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Networks and Systems
Antal sidor491
FörlagSpringer Nature Switzerland AG
ISBN9783032062499

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Dr. Harish Sharma is an Associate professor at Rajasthan Technical University, Kota in the Department of Computer Science & Engineering since 2014. He has worked as an Assistant professor at Govt. Engg. College Jhalawar and Vardhman Mahaveer Open University Kota India. He is the secretary and one of the founder members of the Soft Computing Research Society of India. He is a lifetime member of the Cryptology Research Society of India, ISI, Kolkata. He has contributed to more than 125 papers published in various international journals and conferences.Dr. Abhishek Bhatt is presently working as Assistant Professor in the Centre for Artificial Intelligence, at the Madhav Institute of Technology & Science, Gwalior (Deemed University). He receives his Ph.D. degree in Machine learning and Computer Vision domain from the Indian Institute of Technology Roorkee in 2016. He has contributed to more than 100 papers published in various international journals and conferences. His research areas include AI/ML, Image Processing, spatiotemporal data analysis, and computer vision.Dr. Modi Chirag Navinchandra is working as an Associate Professor in the department of computer science and engineering at National Institute of Technology Goa (NIT Goa), India. He has currently a charge of Dean (R&C) at NIT Goa. He completed his PhD and M.Tech in Computer Engineering from NIT Surat in 2014 and 2010 respectively. His research areas include AI/ML, Cyber Security, Blockchain and smart grid energy management. He has successfully disseminated his research output in premier international conferences and journals with good number of citations. In addition, he holds 5 patents. He has received the Best Review Paper Award (2015) from JNCA, Elsevier, San Diego, USA, Best Paper Award from ICINPro 2021 conference and Young scientist award (2015) in cloud computing specialization by VIFRA, Chennai.Prof. Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artificial Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018).

Spatio-Temporal Crime Detection Using Surveillance Videos in a Federated Setting.- A Hybrid Deep Learning Approach for Car Damage Assessment: Status, Location, and Severity Analysis.- Point Cloud Feature Extraction: Traditional vs.Deep Learning Techniques.- Lipreading for Sending Distress Signals by using Deep Learning Model.- Anisotropic Sensor Network Localization using Neuro Fuzzy Ap-proach for Performance Improvement.- A Novel Method for Gesture Recognition in Autonomous Driving using Pose Estimation.- Evolutionary and Random Search Hybridization for Sentiment Analysis Model Selection.