Machine Vision Inspection Systems, Machine Learning-Based Approaches E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 428

Veröffentlichungsjahr: 2021

Beschreibung

Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day's the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process.

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. 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 machine learning-based approaches.

Leseprobe

Contents

Cover

Title Page

Copyright

Preface

1 Machine Learning-Based Virus Type Classification Using Transmission Electron Microscopy Virus Images

1.1 Introduction

1.2 Related Works

1.3 Methodology

1.4 Results and Discussion

1.5 Conclusion

References

2 Capsule Networks for Character Recognition in Low Resource Languages

2.1 Introduction

2.2 Background Study

2.3 System Design

2.4 Experiments and Results

2.5 Discussion

References

3 An Innovative Extended Method of Optical Pattern Recognition for Medical Images With Firm Accuracy—4f System-Based Medical Optical Pattern Recognition

3.1 Introduction

3.2 Optical Signal Processing

3.3 Extended Medical Optical Pattern Recognition

3.4 Initial 4f System

3.5 Simulation Output

3.6 Complications in Real Time Implementation

3.7 Future Enhancements

References

4 Brain Tumor Diagnostic System— A Deep Learning Application

4.1 Introduction

4.2 Deep Learning

4.3 Brain Tumor Diagnostic System

4.4 Computer-Aided Diagnostic Tool

4.5 Conclusion and Future Enhancements

References

5 Machine Learning for Optical Character Recognition System

5.1 Introduction

5.2 Character Recognition Methods

5.3 Phases of Recognition System

5.4 Post-Processing

5.5 Performance Evaluation

5.6 Applications of OCR Systems

5.7 Conclusion and Future Scope

References

6 Surface Defect Detection Using SVM-Based Machine Vision System with Optimized Feature

6.1 Introduction

6.2 Methodology

6.3 Conclusion

References

7 Computational Linguistics-Based Tamil Character Recognition System for Text to Speech Conversion

7.1 Introduction

7.2 Literature Survey

7.3 Proposed Approach

7.4 Design and Analysis

7.5 Experimental Setup and Implementation

7.6 Conclusion

References

8 A Comparative Study of Different Classifiers to Propose a GONN for Breast Cancer Detection

8.1 Introduction

8.2 Methodology

8.3 Results and Discussion

8.4 Conclusion

References

9 Mexican Sign-Language Static-Alphabet Recognition Using 3D Affine Invariants

9.1 Introduction

9.2 Pattern Recognition

9.3 Experiments

9.4 Results

9.5 Discussion

9.6 Conclusion

Acknowledgments

References

10 Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM

10.1 Introduction

10.2 Experimental Materials and Methodology

10.3 Results and Discussion

10.4 Conclusion

References

11 Applications to Radiography and Thermography for Inspection

11.1 Imaging Technology and Recent Advances

11.2 Radiography and its Role

11.3 History and Discovery of X-Rays

11.4 Interaction of X-Rays With Matter

11.5 Radiographic Image Quality

11.6 Applications of Radiography

References

12 Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques

12.1 Breast Cancer Diagnosis

12.2 Breast Cancer Feature Extraction

12.3 Machine Learning in Breast Cancer Classification

12.4 Image Techniques in Breast Cancer Detection

12.5 Dip-Based Breast Cancer Classification

12.6 RCNNs in Breast Cancer Prediction

12.7 Conclusion and Future Work

References

13 Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques

13.1 Introduction

13.2 Related Work

13.3 Methodology

13.4 Results and Discussion

13.5 Conclusion and Future Enhancement

References

14 A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay

14.1 Introduction

14.2 A Brief Review of the Digital Relay Software

14.3 Formulating the Constrained Multi-Objective Optimization of Software Redundancy Allocation Problem (CMOO-SRAP)

14.4 The Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay

14.5 Simulation Study and Results

14.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 CA (in unit) of different classification techniques.

Chapter 2

Table 2.1 Comparison of related studies.

Table 2.2 Model validation accuracy.

Table 2.3 Classification accuracies within individual alphabets.

Table 2.4 Accuracies of different MNIST models.

Chapter 3

Table 3.1 Categorization of output values.

Chapter 5

Table 5.1 Accuracy rate comparison for various algorithms [8].

Chapter 6

Table 6.1 Details of knot image dataset.

Table 6.2 Details of augmentation required image dataset.

Table 6.3 Confusion matrices for the training and testing sample.

Chapter 7

Table 7.1 Usecase specification.

Chapter 8

Table 8.1 Dataset attribute of WBCD.

Table 8.2 Performance analysis of classification techniques.

Chapter 9

Table 9.1 Related work.

Table 9.2 People data for the participants to acquire dataset 1 using leap motio...

Table 9.3 Random geometric transformations applied to the 3D points representing...

Chapter 10

Table 10.1 Comparing online learning algorithm to use solar cooker.

Table 10.2 SSBC analysis of cooking materials with furious SiO

2

/TiO

2

performance...

Chapter 13

Table 13.1 Summary of results.

Chapter 14

Table 14.1 Software reliability and optimum component composition for different ...

Table 14.2 Optimal combination and mean values of the 3 objectives obtained by t...

Table 14.3 Optimal combination and mean values of the 3 objectives obtained by t...

List of Illustrations

Chapter 1

Figure 1.1 Methodology.

Figure 1.2 Ebola virus images (1–6) with sizes 331 × 152, 254 × 198, 203 × 248, ...

Figure 1.3 Entero virus images (1–6) with sizes 225 × 225, 250 × 201, 225 × 225,...

Figure 1.4 Lassa virus images (1–6) with sizes 251 × 201, 180 × 180, 259 × 194, ...

Figure 1.5 SARS-CoV-2 virus images (1–6) with sizes 225 × 225, 256 × 197, 254 × ...

Figure 1.6 Zika virus images (1-6) with sizes 225 225, 202 × 250, 225 × 225, 211...

Figure 1.7 Classification result by applying LR technique.

Figure 1.8 Classification result by applying NN technique.

Figure 1.9 Classification result by applying kNN technique.

Figure 1.10 Classification result by applying NB technique.

Figure 1.11 Classification result by applying LR technique.

Figure 1.12 Classification result by applying NN technique.

Figure 1.13 Classification result by applying kNN technique.

Figure 1.14 Classification result by applying NB technique.

Figure 1.15 Classification result by applying LR technique.

Figure 1.16 Classification result by applying NN technique.

Figure 1.17 Classification result by applying kNN technique.

Figure 1.18 Classification result by applying NB technique.

Figure 1.19 Classification result by applying LR technique.

Figure 1.20 Classification result by applying NN technique.

Figure 1.21 Classification result by applying kNN technique.

Figure 1.22 Classification result by applying NB technique.

Figure 1.23 Classification result by applying LR technique.

Figure 1.24 Classification result by applying NN technique.

Figure 1.25 Classification result by applying kNN technique.

Figure 1.26 Classification result by applying NB technique.

Chapter 2

Figure 2.1 Siamese network architecture.

Algorithm 1: Data generation

Figure 2.2 Omniglot one-shot learning performance of Siamese networks.

Figure 2.3 Sample 1 classification results.

Figure 2.4 Sample 2 classification results.

Figure 2.5 Omniglot n-shot n-way learning performance.

Figure 2.6 Gurmukhi (left) and Cyrillic (right) alphabets.

Figure 2.7 MNIST n-shot learning performance.

Chapter 3

Figure 3.1 Biconvex lens.

Figure 3.2 Two lens imaging system.

Figure 3.3 Extended 4f system.

Figure 3.4 Image with a detected tumor cells.

Figure 3.5 Reference image for detecting brain tumor cells.

Figure 3.6 Uncorrelation peak indicating the severity of tumor.

Figure 3.7 Correlation peak indicating the normal condition of the patient.

Chapter 4

Figure 4.1 Computer vision techniques.

Figure 4.2 Types of machine learning algorithms.

Figure 4.3 Feed forward neural networks.

Figure 4.4 Back propagation neural networks.

Figure 4.5 Proposed CNN architecture.

Figure 4.6 Sigmoid and ReLU activation functions.

Figure 4.7 FA% of proposed CNN models.

Figure 4.8 Computer aided diagnostic system.

Chapter 5

Figure 5.1 Different areas of character recognition.

Figure 5.2 Profiles of a skewed image

Figure 5.3 Skew corrected text region binarization.

Figure 5.4 Character before and after thinning.

Figure 5.5 Relation between different approaches of Recognition Systems.

Figure 5.6 Holistic and analytic strategies.

Figure 5.7 3-layered neural network.

Chapter 6

Figure 6.1 Knot sample images of seven types named as dry knot, encased knot, ho...

Figure 6.2 Image augmentation (a) original image (b–c) augmented images with lab...

Figure 6.3 Color feature component extracted from sample image.

Figure 6.4 Texture feature component extracted from sample image.

Figure 6.5 Minimum objective function values at particular iteration during trai...

Figure 6.6 Graphical representation of confusion matrix

Figure 6.7 Classification performance indices plot for testing samples.

Chapter 7

Figure 7.1 Text to speech system.

Figure 7.2 UML Usecase diagram.

Figure 7.3 Activity diagram for TTS system.

Figure 7.4 Sub-activity diagram for pre-process module

Figure 7.5 Deployment diagram.

Figure 7.6 Python module requirements.

Figure 7.7 Audio files and Metadata.txt.

Figure 7.8 Audio files and transcriptions.

Figure 7.9 Data preparation.

Figure 7.10 Pre-process function for Tamil dataset.

Figure 7.11 Function for Linear and Mel Spectrogram creation.

Figure 7.12 Main function for Pre-process file.

Figure 7.13 Set of valid symbols.

Figure 7.14 Valid Tamil characters.

Figure 7.15 Tacotron block diagram.

Figure 7.16 CBHG module function.

Figure 7.17 Code for character embedding and encoder.

Figure 7.18 Code for attention module.

Figure 7.19 Code for decoder.

Figure 7.20 Code for Post-processing net.

Figure 7.21 Training code.

Figure 7.22 Train.py execution.

Figure 7.23 Model initialization.

Figure 7.24 Demo server code.

Figure 7.25 Function to call synthesizer.

Figure 7.26 Webpage script.

Figure 7.27 Webpage script function call.

Figure 7.28 Demo server execution.

Figure 7.29 Webpage demo.

Figure 7.30 Input to the model for audio synthesize.

Figure 7.31 Audio output.

Chapter 8

Figure 8.1 The overall methodology of the present work.

Figure 8.2 Methodology for optimizing the genetic algorithm.

Figure 8.3 Working principle of decision tree.

Figure 8.4 Linear regression graph plotted between two features.

Chapter 9

Figure 9.1 Mexican sign language alphabet. The letters (J, K, Ñ, Q, X, Z) that h...

Figure 9.2 In pattern recognition, data is acquired through sensors. To learn an...

Figure 9.3 For one data set, acquisition is performed using the leap motion sens...

Figure 9.4 Setup for data acquisition using the leap motion controller. The sens...

Figure 9.5 The metrics values are shown for each of the 21 letters of static alp...

Figure 9.6 This plot shows the mean value of the metrics over the 21 letters of ...

Figure 9.7 The metrics values are shown for each of the 21 letters of static alp...

Figure 9.8 This plot shows the mean value of the metrics over the 21 letters of ...

Figure 9.9 The metrics values are shown for each of the 21 letters of static alp...

Figure 9.10 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.11 The metrics values are shown for each of the 21 letters of static al...

Figure 9.12 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.13 The metrics values are shown for each of the 21 letters of static al...

Figure 9.14 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.15 The metrics values are shown for each of the 21 letters of static al...

Figure 9.16 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.17 The metrics values are shown for each of the 21 letters of static al...

Figure 9.18 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.19 The metrics values are shown for each of the 21 letters of static al...

Figure 9.20 This plot shows the mean value of the metrics over the 21 letters of...

Figure 9.21 The metrics values are shown for each of the 21 letters of static al...

Figure 9.22 This plot shows the mean value of the metrics over the 21 letters of...

Chapter 10

Figure 10.1 Experimental process views of a SSBC.

Figure 10.2 Schematic diagrams for SSBC.

Figure 10.3 Exposed flow chart in solar cooker control based on adaptive and OSE...

Figure 10.4 Shows sample analysis of 0.5% volume fractions act of parameters by ...

Figure 10.5 Shown sample analysis of 10% volume fractions act of parameters by a...

Figure 10.6 Shows 1 kg mass used in various volume fraction acts of SSBC by the ...

Figure 10.7 Shows various volume fraction acts of SSBC and overall efficiency by...

Chapter 11

Figure 11.1 X ray tube: (a) stationary X-ray tube and (b) rotatory X-ray tube.

Figure 11.2 (a): High SNR: large FOV images: MRI and (b): lower limb angiography...

Figure 11.3 Medical applications of X-rays: (a) Chest PA, (b) Cervical spine Lat...

Figure 11.4 Digital radiography machine: medical application.

Figure 11.5 (a) Barium enema and (b) T-tube cholangiography.

Figure 11.6 Computed tomography machine.

Figure 11.7 Contrast brain angiography.

Figure 11.8 Cardiac CT angiography.

Figure 11.9 (a) CT abdomen angiography and (b) whole abdomen angiography reveali...

Chapter 12

Figure 12.1 The breast cancer detection framework.

Figure 12.2 Image sharpening.

Figure 12.3 Sharpened image.

Figure 12.4 Result of image global thresholding.

Figure 12.5 Synthetically generated image of two dark blobs.

Figure 12.6 RCNN system.

Figure 12.7 Accuracy of training.

Figure 12.8 RCNN result.

Figure 12.9 RCNN output image.

Figure 12.10 Breast cancer detection for cancer.

Figure 12.11 Breast cancer detection for normal.

Chapter 13

Figure 13.1 Generic CBIR system.

Figure 13.2 Image compression framework.

Figure 13.3 Flowchart of proposed methodology.

Figure 13.4 shows Sobel masks. (a) Sub image (b) Sobel mask for horizontal direc...

Figure 13.5 Images used in the investigation.

Figure 13.6 Classification accuracy.

Figure 13.7 Precision.

Figure 13.8 Recall.

Figure 13.9 F Measure.

Chapter 14

Figure 14.1 Schematic representation of a digital relay algorithm using wavelet ...

Figure 14.2 The analytical hierarchy process for the digital relay.

Algorithm 1: Pseudo code representation of the basic firefly algorithm

Algorithm 2: Pseudo Code representation of Discrete Firefly Algorithm

Algorithm 3: Pseudo code representation of the proposed SBPS algorithm.

Figure 14.3 Deviation in reliability values obtained using the proposed Baseline...

Figure 14.4 Deviation in costs obtained with respect to the proposed Baseline DF...

Figure 14.5 Deviation in KLoC obtained with respect to the proposed

Baseline DFA

...

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

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Scrivener Publishing

100 Cummings Center, Suite 541J

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.

Chapter 4 studied about brain tumor diagnosis process on digital images using a convolutional neural network (CNN) as a part of the deep learning model. To classification of brain tumors, eight different CNN models were tested on magnetic resonance imaging (MRI). Additionally, the detailed discussion on machine learning algorithms and deep learning techniques is presented.

Chapter 5 focused on optical character recognition. In this chapter, the detailed study was presented on handwritten identification and classification techniques and their applications. Furthermore, this chapter discussed their limitations along with an overview of the precision rate of Artificial Neural Network-based approaches.

Chapter 6 presented an automated process of detection of defects on wood or metal surface. Generally, monitoring the quality of raw material plays a crucial role in the production of a quality product. Therefore, this chapter developed the classification model using the multiclass support vector machine to identify the defected present into the wood.

Chapter 7 focused computational linguistics towards text recognition and synthesis, speech recognition and synthesis, and conversion between text to speech and vice versa. This chapter branches out towards a text- to-speech system (TTS) which is used for conversion of natural language text into speech distinguishing itself from other systems that render symbolic linguistic representations like phonetic transcriptions into speech. This chapter mainly deals with an intelligible text-to-speech program that allows a visually impaired or a person with a reading disability to familiarize a language.

Chapter 8 deliberated surveyed about breast cancer among Indian females. The survey revealed that only 66.1% of women were diagnosed with cancer and survived. To identify the tumor for breast cancer various machine learning algorithms were adopted in the literature. In this chapter, a comparative study of existing classifiers like support vector clustering (SVC), decision tree classification algorithm (DTC), K-nearest neighbors (KNN), random forest (RF), and multilayer perceptron (MLP) are demonstrated on Wisconsin-breast-cancer-dataset (WBCD) of UCI Machine learning repository.

Chapter 9 focused on communication for hearing impaired people. Since most members of this community use sign language, it is extremely valuable to develop automatized traductors between this language and other spoken languages. This chapter reports the recognition of Mexican sign-language static-alphabet from 3D data acquired from leap motion and MS Kinect 1 sensors. The novelty of this research is the use of six 3D affine moments invariants for sign language recognition.

Chapter 10 presented the solar cooker precise for scientific design. The human interference methods of traditional are exceeding trust for thermal applications and the environment cannot adapt to the variable source. In this chapter, the novel solar cooker has been discussed and based adaptive control through an online Sequential Extreme Learning Machine (OSELM).

Chapter 11 discussed the uses and applications of X-ray images. In this chapter, a detailed study was conducted on radio-diagnosis, nuclear medicine, and radiotherapy remain strong pillars for inspection, diagnosis, and treatment delivery systems. Also, discussed recent advances in artificial intelligence using radiography such as computed tomography.

Chapter 12 addressed the detection and analysis of breast illnesses in mammography images. This chapter presented the use of overlay convolutional neural networks that allow characteristic extraction from the mammography scans which is thereafter fed into a recurrent neural community. Also, this chapter would in actuality assist in tumor localization in case of breast cancers.

Chapter 13 focused on compression of medical images like MRI, ultrasound, and medical-related scans. Generally, voluminous data is embedded in medically produced images from various procedures and it produces images that need more storage space, managing which is difficult. Therefore, this chapter discussed compression of medical images and also techniques to classify the compressed images which are useful in telemedicine.

Chapter 14 presented a computer relays a special-purpose system designed specifically for sensing anomalies in the power system. Since all modern engineered systems, including modern computer relays, are constituted of increased proportions of software sophistication, software reliability assessment has become very important. This chapter discussed a constrained multi-objective formulation of the optimal software reliability allocation problem and thereafter develops a customized Discrete Firefly algorithm (DFA) to solve the aforementioned problem, using computer relay software as a case study.

Muthukumaran Malarvel

Soumya Ranjan Nayak

Prasant Kumar Pattnaik

Surya Narayan Panda

November 2020