Machine Vision Inspection Systems: Image Processing, Concepts, Methodologies, and Applications
- 3h 33m
- Muthukumaran Malarvel, Nittaya Muangnak, Prasant Kumar Pattnaik, Soumya Ranjan Nayak, Surya Narayan Panda
- John Wiley & Sons (US)
This edited book brings together leading researchers, academic scientists and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of image processing and analytics of machine vision for real and industrial application.
Machine vision inspection systems (MVIS) utilized all industrial and non-industrial applications where the execution of their utilities based on the acquisition and processing of images. MVIS can be applicable in industry, governmental, defense, aerospace, remote sensing, medical, and academic/education applications but constraints are different. MVIS entails acceptable accuracy, high reliability, high robustness, and low cost. Image processing is a well-defined transformation between human vision and image digitization, and their techniques are the foremost way to experiment in the MVIS. The digital image technique furnishes improved pictorial information by processing the image data through machine vision perception. Digital image pro¬cessing has widely been used 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), barcode reading and traceability, medical diagnosis, weather forecasting, face recognition, defence and space research, etc. This edited book is designed to address various aspects of recent methodologies, concepts and research plan out to the readers for giving more depth insights for perusing research on machine vision using image processing techniques.
About the Authors
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.
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.
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 Interface.
Nittaya Muangnak is a lecturer at Kasetsart University, Thailand. Her PhD research has been on medical image analysis, particularly retinal fundus image, at Sirindhorn International Institute of Technology, Thammasat University in Thailand.
In this Book
Land-Use Classification with Integrated Data
Indian Sign Language Recognition Using Soft Computing Techniques
Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model
Object Descriptor for Machine Vision
Flood Disaster Management—Risks, Technologies, and Future Directions
Temporal Color Analysis of Avocado Dip for Quality Control
Image and Video Processing for Defect Detection in Key Infrastructure
Methodology for the Detection of Asymptomatic Diabetic Retinopathy
Offline Handwritten Numeral Recognition Using Convolution Neural Network
A Review on Phishing—Machine Vision and Learning Approaches