Muutke küpsiste eelistusi

Machine Vision Inspection Systems, Machine Learning-Based Approaches Volume 2 [Kõva köide]

Edited by , Edited by , Edited by , Edited by
  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Sari: Machine Vision Inspection Systems
  • Ilmumisaeg: 05-Mar-2021
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119786096
  • ISBN-13: 9781119786092
Teised raamatud teemal:
Machine Vision Inspection Systems, Machine Learning-Based Approaches Volume 2Suurem pilt
  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Sari: Machine Vision Inspection Systems
  • Ilmumisaeg: 05-Mar-2021
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119786096
  • ISBN-13: 9781119786092
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119786092.html
Märksõnad:

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.

Preface xiii
1 Machine Learning-Based Virus Type Classification Using Transmission Electron Microscopy Virus Images 1(22)
Kalyan Kumar Jena
Sourav Kumar Bhoi
Soumya Ranjan Nayak
Chittaranjan Mallick
1.1 Introduction
2(1)
1.2 Related Works
3(1)
1.3 Methodology
4(2)
1.4 Results and Discussion
6(10)
1.5 Conclusion
16(1)
References
16(7)
2 Capsule Networks for Character Recognition in Low Resource Languages 23(24)
C. Abeysinghe
I. Perera
D.A. Meedeniya
2.1 Introduction
24(1)
2.2 Background Study
25(3)
2.2.1 Convolutional Neural Networks
25(1)
2.2.2 Related Studies on One-Shot Learning
26(1)
2.2.3 Character Recognition as a One-Shot Task
26(2)
2.3 System Design
28(5)
2.3.1 One-Shot Learning Implementation
31(1)
2.3.2 Optimization and Learning
31(1)
2.3.3 Dataset
32(1)
2.3.4 Training Process
32(1)
2.4 Experiments and Results
33(8)
2.4.1 N-Way Classification
34(3)
2.4.2 Within Language Classification
37(2)
2.4.3 MNIST Classification
39(2)
2.4.4 Sinhala Language Classification
41(1)
2.5 Discussion
41(2)
2.5.1 Study Contributions
41(1)
2.5.2 Challenges and Future Research Directions
42(1)
2.5.3 Conclusion
43(1)
References
43(4)
3 An Innovative Extended Method of Optical Pattern Recognition for Medical Images With Firm Accuracy-4f System-Based Medical Optical Pattern Recognition 47(22)
Dhivya Priya E.L.
D. Jeyabharathi
K.S. Lavanya
S. Thenmozhi
R. Udaiyakumar
A. Sharmila
3.1 Introduction
48(2)
3.1.1 Fourier Optics
48(2)
3.2 Optical Signal Processing
50(5)
3.2.1 Diffraction of Light
50(1)
3.2.2 Biconvex Lens
51(1)
3.2.3 4f System
51(1)
3.2.4 Literature Survey
52(3)
3.3 Extended Medical Optical Pattern Recognition
55(4)
3.3.1 Optical Fourier Transform
55(1)
3.3.2 Fourier Transform Using a Lens
55(1)
3.3.3 Fourier Transform in the Far Field
56(1)
3.3.4 Correlator Signal Processing
56(1)
3.3.5 Image Formation in 4f System
57(1)
3.3.6 Extended Medical Optical Pattern Recognition
58(1)
3.4 Initial 4f System
59(1)
3.4.1 Extended 4f System
59(1)
3.4.2 Setup of 45 Degree
59(1)
3.4.3 Database Creation
59(1)
3.4.4 Superimposition of Diffracted Pattern
60(1)
3.4.5 Image Plane
60(1)
3.5 Simulation Output
60(4)
3.5.1 MATLAB
60(1)
3.5.2 Sample Input Images
61(1)
3.5.3 Output Simulation
61(3)
3.6 Complications in Real Time Implementation
64(1)
3.6.1 Database Creation
64(1)
3.6.2 Accuracy
65(1)
3.6.3 Optical Setup
65(1)
3.7 Future Enhancements
65(1)
References
65(4)
4 Brain Tumor Diagnostic System-A Deep Learning Application 69(22)
T. Kalaiselvi
S.T. Padmapriya
4.1 Introduction
69(6)
4.1.1 Intelligent Systems
69(1)
4.1.2 Applied Mathematics in Machine Learning
70(2)
4.1.3 Machine Learning Basics
72(1)
4.1.4 Machine Learning Algorithms
73(2)
4.2 Deep Learning
75(5)
4.2.1 Evolution of Deep Learning
75(1)
4.2.2 Deep Networks
76(1)
4.2.3 Convolutional Neural Networks
77(3)
4.3 Brain Tumor Diagnostic System
80(6)
4.3.1 Brain Tumor
80(1)
4.3.2 Methodology
80(4)
4.3.3 Materials and Metrics
84(1)
4.3.4 Results and Discussions
85(1)
4.4 Computer-Aided Diagnostic Tool
86(1)
4.5 Conclusion and Future Enhancements
87(1)
References
88(3)
5 Machine Learning for Optical Character Recognition System 91(18)
Gurwinder Kaur
Tanya Garg
5.1 Introduction
91(1)
5.2 Character Recognition Methods
92(1)
5.3 Phases of Recognition System
93(8)
5.3.1 Image Acquisition
93(1)
5.3.2 Defining ROI
94(1)
5.3.3 Pre-Processing
94(1)
5.3.4 Character Segmentation
94(1)
5.3.5 Skew Detection and Correction
95(1)
5.3.6 Binarization
95(2)
5.3.7 Noise Removal
97(1)
5.3.8 Thinning
97(1)
5.3.9 Representation
97(1)
5.3.10 Feature Extraction
98(1)
5.3.11 Training and Recognition
98(3)
5.4 Post-Processing
101(2)
5.5 Performance Evaluation
103(1)
5.5.1 Recognition Rate
103(1)
5.5.2 Rejection Rate
103(1)
5.5.3 Error Rate
103(1)
5.6 Applications of OCR Systems
104(1)
5.7 Conclusion and Future Scope
105(1)
References
105(4)
6 Surface Defect Detection Using SVM-Based Machine Vision System with Optimized Feature 109(20)
Ashok Kumar Patel
Venkata Naresh Mandhala
Dinesh Kumar Anguraj
Soumya Ranjan Nayak
6.1 Introduction
110(3)
6.2 Methodology
113(10)
6.2.1 Data Collection
113(1)
6.2.2 Data Pre-Processing
113(2)
6.2.3 Feature Extraction
115(1)
6.2.4 Feature Optimization
116(3)
6.2.5 Model Development
119(1)
6.2.6 Performance Evaluation
120(3)
6.3 Conclusion
123(1)
References
124(5)
7 Computational Linguistics-Based Tamil Character Recognition System for Text to Speech Conversion 129(26)
S. Suriya
M. Balaji
T.M. Gowtham
Kumar S. Rahul
7.1 Introduction
130(1)
7.2 Literature Survey
130(4)
7.3 Proposed Approach
134(1)
7.4 Design and Analysis
134(2)
7.5 Experimental Setup and Implementation
136(15)
7.6 Conclusion
151(1)
References
151(4)
8 A Comparative Study of Different Classifiers to Propose a GONN for Breast Cancer Detection 155(16)
Ankita Tiwari
Bhawana Sahu
Jagalingam Pushaparaj
Muthukumaran Malarvel
8.1 Introduction
156(1)
8.2 Methodology
157(8)
8.2.1 Dataset
157(2)
8.2.2 Linear Regression
159(2)
8.2.2.1 Correlation
160(1)
8.2.2.2 Covariance
160(1)
8.2.3 Classification Algorithm
161(14)
8.2.3.1 Support Vector Machine
161(1)
8.2.3.2 Random Forest Classifier
162(1)
8.2.3.3 K-Nearest Neighbor Classifier
163(1)
8.2.3.4 Decision Tree Classifier
163(1)
8.2.3.5 Multi-Layered Perceptron
164(1)
8.3 Results and Discussion
165(4)
8.4 Conclusion
169(1)
References
169(2)
9 Mexican Sign-Language Static-Alphabet Recognition Using 3D Affine Invariants 171(22)
Guadalupe Carmona-Arroyo
Homero V. Rios-Figueroa
Martha Lorena Ayendano-Garrido
9.1 Introduction
171(4)
9.2 Pattern Recognition
175(2)
9.2.1 3D Affine Invariants
175(2)
9.3 Experiments
177(5)
9.3.1 Participants
179(1)
9.3.2 Data Acquisition
179(1)
9.3.3 Data Augmentation
179(2)
9.3.4 Feature Extraction
181(1)
9.3.5 Classification
181(1)
9.4 Results
182(6)
9.4.1 Experiment 1
182(2)
9.4.2 Experiment 2
184(1)
9.4.3 Experiment 3
184(4)
9.5 Discussion
188(1)
9.6 Conclusion
189(1)
Acknowledgments
190(1)
References
190(3)
10 Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM 193(26)
S. Shanmugan
F.A. Essa
J. Nagaraj
Shilpa Itnal
10.1 Introduction
194(2)
10.2 Experimental Materials and Methodology
196(14)
10.2.1 Furious SiO2/TiO2 Nanoparticle Analysis of SSBC Performance Methods
196(2)
10.2.2 Introduction for OSELM by Use of Solar Cooker
198(1)
10.2.3 Online Sequential Extreme Learning Machine (OSELM) Approach for Solar Cooker
199(1)
10.2.4 OSELM Neural Network Adaptive Controller on Novel Design
199(1)
10.2.5 Binary Search Tree Analysis of Solar Cooker
200(5)
10.2.6 Tree Traversal of the Solar Cooker
205(1)
10.2.7 Simulation Model of Solar Cooker Results
206(1)
10.2.8 Program
207(3)
10.3 Results and Discussion
210(2)
10.4 Conclusion
212(2)
References
214(5)
11 Applications to Radiography and Thermography for Inspection 219(22)
Inderjeet Singh Sandhu
Chanchal Kaushik
Mansi Chitkara
11.1 Imaging Technology and Recent Advances
220(1)
11.2 Radiography and its Role
220(1)
11.3 History and Discovery of X-Rays
221(1)
11.4 Interaction of X-Rays With Matter
222(1)
11.5 Radiographic Image Quality
222(3)
11.6 Applications of Radiography
225(11)
11.6.1 Computed Radiography (CR)/Digital Radiography (DR)
225(2)
11.6.2 Fluoroscopy
227(1)
11.6.3 DEXA
228(1)
11.6.4 Computed Tomography
229(2)
11.6.5 Industrial Radiography
231(3)
11.6.6 Thermography
234(1)
11.6.7 Veterinary Imaging
235(1)
11.6.8 Destructive Testing
235(1)
11.6.9 Night Vision
235(1)
11.6.10 Conclusion
236(1)
References
236(5)
12 Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques 241(22)
M. Pavithra
R. Rajmohan
T. Ananth Kumar
R. Ramya
12.1 Breast Cancer Diagnosis
242(1)
12.2 Breast Cancer Feature Extraction
243(2)
12.3 Machine Learning in Breast Cancer Classification
245(1)
12.4 Image Techniques in Breast Cancer Detection
246(2)
12.5 Dip-Based Breast Cancer Classification
248(7)
12.6 RCNNs in Breast Cancer Prediction
255(5)
12.7 Conclusion and Future Work
260(1)
References
261(2)
13 Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques 263(24)
Vamsidhar Enireddy
C. Karthikeyan
T. Rajesh Kumar
Ashok Bekkanti
13.1 Introduction
264(1)
13.2 Related Work
265(4)
13.2.1 Approaches in Content-Based Image Retrieval (CBIR)
265(1)
13.2.2 Medical Image Compression
266(1)
13.2.3 Image Retrieval for Compressed Medical Images
267(1)
13.2.4 Feature Selection in CBIR
268(1)
13.2.5 CBIR Using Neural Network
268(1)
13.2.6 Classification of CBIR
269(1)
13.3 Methodology
269(8)
13.3.1 Huffman Coding
270(1)
13.3.2 Haar Wavelet
271(2)
13.3.3 Sobel Edge Detector
273(1)
13.3.4 Gabor Filter
273(3)
13.3.5 Proposed Hybrid CS-PSO Algorithm
276(1)
13.4 Results and Discussion
277(5)
13.5 Conclusion and Future Enhancement
282(1)
13.5.1 Conclusion
282(1)
13.5.2 Future Work
283(1)
References
283(4)
14 A Novel Discrete Firefly Algorithm for Constrained Multi- Objective Software Reliability Assessment of Digital Relay 287(36)
Madhusudana Rao Nalluri
K. Kannan
Diptendu Sinha Roy
14.1 Introduction
288(3)
14.2 A Brief Review of the Digital Relay Software
291(2)
14.3 Formulating the Constrained Multi-Objective Optimization of Software Redundancy Allocation Problem (CMOO-SRAP)
293(4)
14.3.1 Mathematical Formulation
294(3)
14.4 The Novel Discrete Firefly Algorithm for Constrained Multi- Objective Software Reliability Assessment of Digital Relay
297(8)
14.4.1 Basic Firefly Algorithm
298(1)
14.4.2 The Modified Discrete Firefly Algorithm
299(4)
14.4.2.1 Generating Initial Population
299(1)
14.4.2.2 Improving Solutions
299(2)
14.4.2.3 Illustrative Example
301(2)
14.4.3 Similarity-Based Parent Selection (SBPS)
303(2)
14.4.4 Solution Encoding for the CMOO-SRAP for Digital Relay Software
305(1)
14.5 Simulation Study and Results
305(12)
14.5.1 Simulation Environment
305(1)
14.5.2 Simulation Parameters
306(1)
14.5.3 Configuration of Solution Vectors for the CMOO- SRAP for Digital Relay
306(1)
14.5.4 Results and Discussion
306(11)
14.6 Conclusion
317(1)
References
317(6)
Index 323
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 PhD (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.



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.