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.
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.
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.
