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Submit a ProposalMachine Vision Inspection Systems, Volume 2
 | Machine Learning-Based Approaches Edited by Muthukumaran Malarvel, Soumya Ranjan Nayak, Prasant Kumar Pattnaik and Surya Narayan Panda Copyright: 2021 | Expected Pub Date:2021// ISBN: 9781119786092 | Hardcover | 344 pages | 160 illustrations Price: $225 USD  |
One Line Description
The overall aim of the book is to extend recent concepts, methodologies, and empirical research advances of various machine vision inspection systems through image processing approaches.
Audience
The book will have much interest in the industrial engineering manufacturing sector, especially the non-destructive testing industries such as defence, aerospace, remote sensing, defect/fault inspection specialists, medical diagnosis labs and instrument makers. Industry engineers and as well researchers in computer science associated with image processing, machine vision and pattern recognition, artificial intelligence, data analytics, will find this book valuable.
The book will have much interest in the industrial engineering manufacturing sector, especially the non-destructive testing industries such as defence, aerospace, remote sensing, defect/fault inspection specialists, medical diagnosis labs and instrument makers. Industry engineers and as well researchers in computer science associated with image processing, machine vision and pattern recognition, artificial intelligence, data analytics, will find this book valuable.
Description
Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes the image processing, machine vision and pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Recently, the current automated vision research on machine inspection has gained more popularity with researchers and engineers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examinations during the inspection process, leading to potential disaster. Machine Vision Inspection Systems (MVIS) is better able to avoid false assessment.
This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. The book is designed to address various aspects of recent methodologies, concepts, and research so readers will gain more in-depth insights in machine vision using machine learning-based approaches.
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Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes the image processing, machine vision and pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Recently, the current automated vision research on machine inspection has gained more popularity with researchers and engineers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examinations during the inspection process, leading to potential disaster. Machine Vision Inspection Systems (MVIS) is better able to avoid false assessment.
This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. The book is designed to address various aspects of recent methodologies, concepts, and research so readers will gain more in-depth insights in machine vision using machine learning-based approaches.
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Author / Editor Details
Muthukumaran Malarvel obtained his PhD in Digital Image Processing and he is currently working as an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Punjab, India. His research interests include digital image processing, machine vision systems, image statistical analysis & feature extraction, and machine learning algorithms.
Muthukumaran Malarvel obtained his PhD in Digital Image Processing and he is currently working as an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Punjab, India. His research interests include digital image processing, machine vision systems, image statistical analysis & feature extraction, and machine learning algorithms.
Soumya Ranjan Nayak obtained his PhD in computer science and engineering from the Biju Patnaik University of Technology, India. He has more than a decade of teaching and research experience and currently is working as an Assistant Professor, Amity University, Noida, India. His research interests include image analysis on fractal geometry, color and texture analysis jointly and separately.
Prasant Kumar Pattnaik Ph.D. (Computer Science), Fellow IETE, Senior Member IEEE is a Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He has more than a decade of teaching and research experience. His areas of interest include Mobile Computing, Cloud Computing, Cyber Security, Intelligent Systems and Brain Computer Inteface
Surya Narayan Panda is a Professor and Director Research at Chitkara University, Punjab, India. His areas of interest include Cybersecurity, Networking, Advanced Computer Networks, Machine Learning, and Artificial Intelligence. He has developed the prototype of Smart Portable Intensive Care Unit through which the doctor can provide immediate virtual medical assistance to emergency cases in the ambulance. He is currently involved in designing different healthcare devices for real-time issues using AI and ML.
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