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Machine Vision Inspection Systems, Image Processing, Concepts, Methodologies, and Applications Volume 1 [Kõva köide]

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  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Sari: Machine Vision Inspection Systems
  • Ilmumisaeg: 07-Jul-2020
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119681804
  • ISBN-13: 9781119681809
Teised raamatud teemal:
Machine Vision Inspection Systems, Image Processing, Concepts, Methodologies, and Applications Volume 1Suurem pilt
  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Sari: Machine Vision Inspection Systems
  • Ilmumisaeg: 07-Jul-2020
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119681804
  • ISBN-13: 9781119681809
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119681809.html
Märksõnad:

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 pro­cessing 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.
Preface xi
1 Land-Use Classification with Integrated Data
1(36)
D. A. Meedeniya
J. A. A. M. Jayanetti
M. D. N. Dilini
M. H. Wickramapala
J. H. Madushanka
1.1 Introduction
2(1)
1.2 Background Study
3(3)
1.2.1 Overview of Land-Use and Land-Cover Information
3(1)
1.2.2 Geographical Information Systems
4(1)
1.2.3 GIS-Related Data Types
4(1)
1.2.3.1 Point Data Sets
4(1)
1.2.3.2 Aerial Data Sets
5(1)
1.2.4 Related Studies
6(1)
1.3 System Design
6(4)
1.4 Implementation Details
10(15)
1.4.1 Materials
10(1)
1.4.2 Preprocessing
11(1)
1.4.3 Built-Up Area Extraction
11(1)
1.4.4 Per-Pixel Classification
12(2)
1.4.5 Clustering
14(1)
1.4.6 Segmentation
14(2)
1.4.7 Object-Based Image Classification
16(4)
1.4.8 Foursquare Data Preprocessing and Quality Analysis
20(1)
1.4.9 Integration of Satellite Images with Foursquare Data
21(1)
1.4.10 Building Block Identification
21(1)
1.4.11 Overlay of Foursquare Points
22(1)
1.4.12 Visualization of Land Usage
23(1)
1.4.13 Common Platform Development
23(2)
1.5 System Evaluation
25(6)
1.5.1 Experimental Evaluation Process
25(3)
1.5.2 Evaluation of the Classification Using Base Error Matrix
28(3)
1.6 Discussion
31(3)
1.6.1 Contribution of the Proposed Approach
31(1)
1.6.2 Limitations of the Data Sets
32(1)
1.6.3 Future Research Directions
33(1)
1.7 Conclusion
34(3)
References
35(2)
2 Indian Sign Language Recognition Using Soft Computing Techniques
37(30)
Ashok Kumar Sahoo
Pradeepta Kumar Sarangi
Parul Goyal
2.1 Introduction
37(1)
2.2 Related Works
38(8)
2.2.1 The Domain of Sign Language
39(2)
2.2.2 The Data Acquisition Methods
41(1)
2.2.3 Preprocessing Steps
42(1)
2.2.3.1 Image Restructuring
43(1)
2.2.3.2 Skin Color Detection
43(1)
2.2.4 Methods of Feature Extraction Used in the Experiments
44(1)
2.2.5 Classification Techniques
45(1)
2.2.5.1 K-Nearest Neighbor
45(1)
2.2.5.2 Neural Network Classifier
45(1)
2.2.5.3 Naive Bayes Classifier
46(1)
2.3 Experiments
46(17)
2.3.1 Experiments on ISL Digits
46(1)
2.3.1.1 Results and Discussions on the First Experiment
47(2)
2.3.1.2 Results and Discussions on Second Experiment
49(2)
2.3.2 Experiments on ISL Alphabets
51(1)
2.3.2.1 Experiments with Single-Handed Alphabet Signs
51(1)
2.3.2.2 Results of Single-Handed Alphabet Signs
52(1)
2.3.2.3 Experiments with Double-Handed Alphabet Signs
53(1)
2.3.2.4 Results on Double-Handed Alphabets
54(4)
2.3.3 Experiments on ISL Words
58(1)
2.3.3.1 Results on ISL Word Signs
59(4)
2.4 Summary
63(4)
References
63(4)
3 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model
67(18)
Kalyan Kumar Jena
Sasmita Mishra
Sarojananda Mishra
Sourav Kumar Bhoi
3.1 Introduction
68(1)
3.2 Related Work
69(1)
3.3 Proposed Model
70(2)
3.4 Results and Discussion
72(5)
3.5 Conclusion
77(8)
References
78(7)
4 Object Descriptor for Machine Vision
85(30)
Aparna S. Murthy
Salah Rabba
4.1 Outline
85(2)
4.2 Chain Codes
87(2)
4.3 Polygonal Approximation
89(3)
4.4 Moments
92(4)
4.5 HU Invariant Moments
96(1)
4.6 Zernike Moments
97(1)
4.7 Fourier Descriptors
98(1)
4.8 Quadtree
99(3)
4.9 Conclusion
102(13)
References
114(1)
5 Flood Disaster Management: Risks, Technologies, and Future Directions
115(32)
Hafiz Suliman Munawar
5.1 Flood Management
115(9)
5.1.1 Introduction
115(1)
5.1.2 Global Flood Risks and Incidents
116(2)
5.1.3 Causes of Floods
118(1)
5.1.4 Floods in Pakistan
119(2)
5.1.5 Floods in Australia
121(2)
5.1.6 Why Floods are a Major Concern
123(1)
5.2 Existing Disaster Management Systems
124(5)
5.2.1 Introduction
124(1)
5.2.2 Disaster Management Systems Used Around the World
124(1)
5.2.2.1 Disaster Management Model
125(1)
5.2.2.2 Disaster Risk Analysis System
126(1)
5.2.2.3 Geographic Information System
126(1)
5.2.2.4 Web GIS
126(1)
5.2.2.5 Remote Sensing
127(1)
5.2.2.6 Satellite Imaging
127(1)
5.2.2.7 Global Positioning System for Imaging
128(1)
5.2.3 Gaps in Current Disaster Management Technology
128(1)
5.3 Advancements in Disaster Management Technologies
129(8)
5.3.1 Introduction
129(1)
5.3.2 AI and Machine Learning for Disaster Management
130(1)
5.3.2.1 AIDR
130(1)
5.3.2.2 Warning Systems
130(1)
5.3.2.3 QCRI
131(1)
5.3.2.4 The Concern
131(1)
5.3.2.5 BlueLineGrid
131(1)
5.3.2.6 Google Maps
132(1)
5.3.2.7 RADARS AT-1
132(1)
5.3.3 Recent Research in Disaster Management
132(5)
5.3.4 Conclusion
137(1)
5.4 Proposed System
137(10)
5.4.1 Image Acquisition Through UAV
138(1)
5.4.2 Preprocessing
138(1)
5.4.3 Landmarks Detection
138(1)
5.4.3.1 Buildings
139(1)
5.4.3.2 Roads
139(1)
5.4.4 Flood Detection
140(1)
5.4.4.1 Feature Matching
140(1)
5.4.4.2 Flood Detection Using Machine Learning
141(2)
5.4.5 Conclusion
143(1)
References
143(4)
6 Temporal Color Analysis of Avocado Dip for Quality Control
147(12)
Hotnero V. Rios-Figueroa
Micloth Lopez del Castillo-Lozano
Elvia K. Ramirez-Gomez
Ericka J. Rechy-Ramirez
6.1 Introduction
147(1)
6.2 Materials and Methods
148(1)
6.3 Image Acquisition
149(1)
6.4 Image Processing
150(1)
6.5 Experimental Design
150(1)
6.5.1 First Experimental Design
150(1)
6.5.2 Second Experimental Design
151(1)
6.6 Results and Discussion
151(5)
6.6.1 First Experimental Design (RGB Color Space)
151(1)
6.6.2 Second Experimental Design (L*a*b* Color Space)
152(4)
6.7 Conclusion
156(3)
References
156(3)
7 Image and Video Processing for Defect Detection in Key Infrastructure
159(20)
Hafiz Suliman Munawar
7.1 Introduction
160(1)
7.2 Reasons for Defective Roads and Bridges
161(1)
7.3 Image Processing for Defect Detection
162(7)
7.3.1 Feature Extraction
162(1)
7.3.2 Morphological Operators
163(1)
7.3.3 Cracks Detection
164(1)
7.3.4 Potholes Detection
165(1)
7.3.5 Water Puddles Detection
166(1)
7.3.6 Pavement Distress Detection
167(2)
7.4 Image-Based Defect Detection Methods
169(3)
7.4.1 Thresholding Techniques
170(1)
7.4.2 Edge Detection Techniques
170(1)
7.4.3 Wavelet Transform Techniques
171(1)
7.4.4 Texture Analysis Techniques
171(1)
7.4.5 Machine Learning Techniques
172(1)
7.5 Factors Affecting the Performance
172(1)
7.5.1 Lighting Variations
173(1)
7.5.2 Small Database
173(1)
7.5.3 Low-Quality Data
173(1)
7.6 Achievements and Issues
173(1)
7.6.1 Achievements
174(1)
7.6.2 Issues
174(1)
7.7 Conclusion
174(5)
References
175(4)
8 Methodology for the Detection of Asymptomatic Diabetic Retinopathy
179(18)
Jaskirat Kaur
Deepti Mittal
8.1 Introduction
180(1)
8.2 Key Steps of Computer-Aided Diagnostic Methods
181(2)
8.3 DR Screening and Grading Methods
183(5)
8.4 Key Observations from Literature Review
188(1)
8.5 Design of Experimental Methodology
189(3)
8.6 Conclusion
192(5)
References
193(4)
9 Offline Handwritten Numeral Recognition Using Convolution Neural Network
197(16)
Abhisek Sethy
Prashanta Kumar Patra
Soumya Ranjan Nayak
9.1 Introduction
198(1)
9.2 Related Work Done
199(2)
9.3 Data Set Used for Simulation
201(1)
9.4 Proposed Model
202(2)
9.5 Result Analysis
204(3)
9.6 Conclusion and Future Work
207(6)
References
209(4)
10 A Review on Phishing--Machine Vision and Learning Approaches
213(11)
Hemamalini Siranjeevi
Swaminathart Venkatraman
Kannan Krithivasan
10.1 Introduction
213(1)
10.2 Literature Survey
214(3)
10.2.1 Content-Based Approaches
214(1)
10.2.2 Heuristics-Based Approaches
215(1)
10.2.3 Blacklist-Based Approaches
215(1)
10.2.4 Whitelist-Based Approaches
216(1)
10.2.5 CANTINA-Based Approaches
216(1)
10.2.6 Image-Based Approaches
216(1)
10.3 Role of Data Mining in Antiphishing
217(7)
10.3.1 Phishing Detection
219(1)
10.3.2 Phishing Prevention
220(2)
10.3.3 Training and Education
222(1)
10.3.4 Phishing Recovery and Avoidance
222(1)
10.3.5 Visual Methods
223(1)
10.4 Conclusion
224(1)
Acknowledgments 224(1)
References 224(7)
Index 231
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.