Machine Vision Inspection Systems, Machine Learning-Based Approaches. Группа авторов
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Scrivener Publishing
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Beverly MA, 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Machine Vision Inspection Systems, Volume 2
Machine Learning-Based Approaches
Edited by
Muthukumaran Malarvel
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Soumya Ranjan Nayak
Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
Prasant Kumar Pattnaik
School of Computer Engineering, KIIT Deemed to be University, India
Surya Narayan Panda
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2021 Scrivener Publishing LLC
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-78609-2
Cover image: Pixabay.Com
Cover design by: Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
The edited book aims to bring 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 for educators, practitioners and researchers 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 machine learning-based approaches of machine vision for real and industrial application. The book is organized into fourteen chapters.
Chapter 1 deliberated about various dangerous infectious viruses affect human society with a detailed analysis of transmission electron microscopy virus images (TEMVIs). In this chapter, several TEMVIs such as Ebola virus (EV), Enterovirus (ENV), Lassa virus (LV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Zika virus (ZV), etc. are analyzed. The ML-based approach mainly focuses on the classification techniques such as Logistic Regression (LR), Neural Network (NN), k-Nearest Neighbors (kNN) and Naive Bayes (NB) for the processing of TEMVIs.
Chapter 2 focused to identify and differentiate handwriting characters using deep neural networks. As a solution to the character recognition problem in low resource languages, this chapter proposes a model that replicates the human cognition ability to learn with small datasets. The proposed solution is a Siamese neural network which bestows capsules and convolutional units to get a thorough understanding of the image. Further, this chapter attests that the capsule-based Siamese network could learn abstract knowledge about different characters which could be extended to unforeseen characters.
Chapter 3 presented Optics growth with the development of lens in terms of accuracy. The 4f-based optical system is used as a benchmark to develop a firm system for medical applications. This method performing transforms with the optical system helps in improving accuracy. The image of the patient placed in the object plane is exposed to optical rays, the biconvex lens between the object and Fourier Plane performs an optical Fourier transform. This system indicating the normal or abnormal condition of the patient and helps in high-speed pattern recognition with optical signals.