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Object Detection by Stereo Vision Images [Kõva köide]

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  • Formaat: Hardback, 288 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 06-Nov-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119842190
  • ISBN-13: 9781119842194
Teised raamatud teemal:
Object Detection by Stereo Vision ImagesSuurem pilt
  • Formaat: Hardback, 288 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 06-Nov-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119842190
  • ISBN-13: 9781119842194
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119842194.html
Märksõnad:
OBJECT DETECTION BY STEREO VISION IMAGES

Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers.

Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems.

Audience

Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

Preface xiii
1 Data Conditioning for Medical Imaging
1(32)
Shahzia Sayyad
Deepti Nikumbh
Dhruvi Lalit Jain
Prachi Dhiren Khatri
Alok Saratchandra Panda
Rupesh Ravindra Joshi
1.1 Introduction
2(1)
1.2 Importance of Image Preprocessing
2(1)
1.3 Introduction to Digital Medical Imaging
3(3)
1.3.1 Types of Medical Images for Screening
4(1)
1.3.1.1 X-rays
4(1)
1.3.1.2 Computed Tomography (CT) Scan
4(1)
1.3.1.3 Ultrasound
4(1)
1.3.1.4 Magnetic Resonance Imaging (MRI)
5(1)
1.3.1.5 Positron Emission Tomography (PET) Scan
5(1)
1.3.1.6 Mammogram
5(1)
1.3.1.7 Fluoroscopy
5(1)
1.3.1.8 Infrared Thermography
6(1)
1.4 Preprocessing Techniques of Medical Imaging Using Python
6(7)
1.4.1 Medical Image Preprocessing
6(1)
1.4.1.1 Reading the Image
7(1)
1.4.1.2 Resizing the Image
7(1)
1.4.1.3 Noise Removal
8(1)
1.4.1.4 Filtering and Smoothing
9(2)
1.4.1.5 Image Segmentation
11(2)
1.5 Medical Image Processing Using Python
13(7)
1.5.1 Medical Image Processing Methods
16(1)
1.5.1.1 Image Formation
17(2)
1.5.1.2 Image Enhancement
19(1)
1.5.1.3 Image Analysis
19(1)
1.5.1.4 Image Visualization
19(1)
1.5.1.5 Image Management
19(1)
1.6 Feature Extraction Using Python
20(4)
1.7 Case Study on Throat Cancer
24(5)
1.7.1 Introduction
24(1)
1.7.1.1 HSI System
25(1)
1.7.1.2 The Adaptive Deep Learning Method Proposed
25(2)
1.7.2 Results and Findings
27(1)
1.7.3 Discussion
28(1)
1.7.4 Conclusion
29(1)
1.8 Conclusion
29(4)
References
30(1)
Additional Reading
31(1)
Key Terms and Definition
32(1)
2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study
33(24)
Shravani Nitnbolkar
Anuradha Thakare
Subhradeep Mitra
Omkar Biranje
Anant Sutar
2.1 Introduction
33(2)
2.2 Literature Review
35(6)
2.3 Learning Methods
41(2)
2.3.1 Machine Learning
41(1)
2.3.2 Deep Learning
42(1)
2.3.3 Transfer Learning
42(1)
2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques
43(9)
2.4.1 Dataset Description
43(1)
2.4.2 Evaluation Platform
44(1)
2.4.3 Training Process
44(2)
2.4.4 Model Evaluation of CNN Classifier
46(1)
2.4.5 Mathematical Model
47(1)
2.4.6 Parameter Optimization
47(3)
2.4.7 Performance Metrics
50(2)
2.5 Conclusion
52(5)
References
53(4)
3 Contamination Monitoring System Using IOT and GIS
57(16)
Kavita R. Singh
Ravi Wasalwar
Ajit Dharmik
Deepshikha Tiwari
3.1 Introduction
58(1)
3.2 Literature Survey
58(2)
3.3 Proposed Work
60(1)
3.4 Experimentation and Results
61(3)
3.4.1 Experimental Setup
61(3)
3.5 Results
64(6)
3.6 Conclusion
70(3)
Acknowledgement
71(1)
References
71(2)
4 Video Error Concealment Using Particle Swarm Optimization
73(26)
P.K. Rajani
Arti Khaparde
4.1 Introduction
74(1)
4.2 Proposed Research Work Overview
75(1)
4.3 Error Detection
75(2)
4.4 Frame Replacement Video Error Concealment Algorithm
77(1)
4.5 Research Methodology
77(6)
4.5.1 Particle Swarm Optimization
78(1)
4.5.2 Spatio-Temporal Video Error Concealment Method
78(1)
4.5.3 Proposed Modified Particle Swarm Optimization Algorithm
79(4)
4.6 Results and Analysis
83(12)
4.6.1 Single Frame With Block Error Analysis
85(1)
4.6.2 Single Frame With Random Error Analysis
86(2)
4.6.3 Multiple Frame Error Analysis
88(3)
4.6.4 Sequential Frame Error Analysis
91(2)
4.6.5 Subjective Video Quality Analysis for Color Videos
93(1)
4.6.6 Scene Change of Videos
94(1)
4.7 Conclusion
95(2)
4.8 Future Scope
97(2)
References
97(2)
5 Enhanced Image Fusion with Guided Filters
99(12)
Nalini Jagtap
Sudeep D. Thepade
5.1 Introduction
100(1)
5.2 Related Works
100(2)
5.3 Proposed Methodology
102(2)
5.3.1 System Model
102(2)
5.3.2 Steps of the Proposed Methodology
104(1)
5.4 Experimental Results
104(4)
5.4.1 Entropy
104(1)
5.4.2 Peak Signal-to-Noise Ratio
105(2)
5.4.3 Root Mean Square Error
107(1)
5.4.3.1 QAB/F
108(1)
5.5 Conclusion
108(3)
References
109(2)
6 Deepfake Detection Using LSTM-Based Neural Network
111(10)
Tejaswini Yesugade
Shrikant Kokate
Sarjana Patil
Ritik Varma
Sejal Pawar
6.1 Introduction
111(1)
6.2 Related Work
112(1)
6.2.1 Deepfake Generation
112(1)
6.2.2 LSTM and CNN
112(1)
6.3 Existing System
113(1)
6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking
113(1)
6.3.2 Detection Using Inconsistence in Head Pose
113(1)
6.3.3 Exploiting Visual Artifacts
113(1)
6.4 Proposed System
114(3)
6.4.1 Dataset
114(1)
6.4.2 Preprocessing
114(1)
6.4.3 Model
115(2)
6.5 Results
117(2)
6.6 Limitations
119(1)
6.7 Application
119(1)
6.8 Conclusion
119(2)
References
119(2)
7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey
121(26)
Kavita Shinde
Anuradha Thakare
7.1 Introduction
121(2)
7.2 Related Work
123(6)
7.3 Evaluation of Related Research
129(1)
7.4 General Framework for Fetal Brain Abnormality Classification
129(12)
7.4.1 Image Acquisition
130(1)
7.4.2 Image Pre-Processing
130(1)
7.4.2.1 Image Thresholding
130(1)
7.4.2.2 Morphological Operations
131(1)
7.4.2.3 Hole Filling and Mask Generation
131(1)
7.4.2.4 MRI Segmentation for Fetal Brain Extraction
132(1)
7.4.3 Feature Extraction
132(1)
7.4.3.1 Gray-Level Co-Occurrence Matrix
133(1)
7.4.3.2 Discrete Wavelet Transformation
133(1)
7.4.3.3 Gabor Filters
134(1)
7.4.3.4 Discrete Statistical Descriptive Features
134(1)
7.4.4 Feature Reduction
134(1)
7.4.4.1 Principal Component Analysis
135(1)
7.4.4.2 Linear Discriminant Analysis
136(1)
7.4.4.3 Non-Linear Dimensionality Reduction Techniques
137(1)
7.4.5 Classification by Using Machine Learning Classifiers
137(1)
7.4.5.1 Support Vector Machine
138(1)
7.4.5.2 K-Nearest Neighbors
138(1)
7.4.5.3 Random Forest
139(1)
7.4.5.4 Linear Discriminant Analysis
139(1)
7.4.5.5 Naive Bayes
139(1)
7.4.5.6 Decision Tree (DT)
140(1)
7.4.5.7 Convolutional Neural Network
140(1)
7.5 Performance Metrics for Research in Fetal Brain Analysis
141(1)
7.6 Challenges
142(1)
7.7 Conclusion and Future Works
142(5)
References
143(4)
8 Analysis of COVID-19 Data Using Machine Learning Algorithm
147(12)
Chinnaiah Kotadi
K. Mithun Chakravarthi
Srihari Chintha
Kapil Gupta
8.1 Introduction
147(1)
8.2 Pre-Processing
148(1)
8.3 Selecting Features
149(3)
8.4 Analysis of COVID-19-Confirmed Cases in India
152(4)
8.4.1 Analysis to Highest COVID-19-Confirmed Case States in India
153(1)
8.4.2 Analysis to Highest COVID-19 Death Rate States in India
153(1)
8.4.3 Analysis to Highest COVID-19 Cured Case States in India
154(1)
8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State
155(1)
8.5 Linear Regression Used for Predicting Daily Wise COVID-19 Cases in Maharashtra
156(1)
8.6 Conclusion
157(2)
References
157(2)
9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering
159(12)
Manish Sharma
Rutuja Deshmukh
9.1 Introduction
160(2)
9.2 Related Work
162(2)
9.3 Recommender Systems and Collaborative Filtering
164(1)
9.4 Proposed Methodology
165(2)
9.5 Experiment Analysis
167(1)
9.6 Conclusion
168(3)
References
168(3)
10 Virtual Moratorium System
171(14)
Manisha Bhende
Muzasarali Badger
Pranish Kumbhar
Vedanti Bhatkar
Payal Chavan
10.1 Introduction
172(1)
10.1.1 Objectives
172(1)
10.2 Literature Survey
172(1)
10.2.1 Virtual Assistant--BLU
172(1)
10.2.2 HDFC Ask EVA
173(1)
10.3 Methodologies of Problem Solving
173(1)
10.4 Modules
174(2)
10.4.1 Chatbot
174(1)
10.4.2 Android Application
175(1)
10.4.3 Web Application
175(1)
10.5 Detailed Flow of Proposed Work
176(2)
10.5.1 System Architecture
176(1)
10.5.2 DFD Level 1
177(1)
10.6 Architecture Design
178(3)
10.6.1 Main Server
178(1)
10.6.2 Chatbot
178(2)
10.6.3 Database Architecture
180(1)
10.6.4 Web Scraper
180(1)
10.7 Algorithms Used
181(1)
10.7.1 AES-256 Algorithm
181(1)
10.7.2 Rasa NLU
181(1)
10.8 Results
182(1)
10.9 Discussions
183(2)
10.9.1 Applications
183(1)
10.9.2 Future Work
183(1)
10.9.3 Conclusion
183(1)
References
183(2)
11 Efficient Land Cover Classification for Urban Planning
185(10)
Vandana Tulshidas Chavan
Sanjeev J. Wagh
11.1 Introduction
185(4)
11.2 Literature Survey
189(2)
11.3 Proposed Methodology
191(1)
11.4 Conclusion
192(3)
References
192(3)
12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review
195(12)
Pradnya Patil
Sanjeev J. Wagh
12.1 Introduction
196(1)
12.2 Literature Survey
196(5)
12.3 Problem Statement and Objectives
201(1)
12.3.1 Problem Statement
201(1)
12.3.2 Objectives
201(1)
12.4 Proposed Methodology
202(2)
12.4.1 Pre-Processing
202(1)
12.4.2 Feature Extraction
203(1)
12.4.3 Classification
203(1)
12.5 Conclusion
204(3)
References
204(3)
13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering
207(20)
Anupama Patil
Manisha Bhende
Suvarna Patil
P. P. Shevatekar
13.1 Introduction
208(2)
13.2 Related Work
210(3)
13.3 Distance Measurement Using Stereo Vision
213(5)
13.3.1 Calibration of the Camera
215(1)
13.3.2 Stereo Image Rectification
215(1)
13.3.3 Disparity Estimation and Stereo Matching
216(1)
13.3.4 Measurement of Distance
217(1)
13.4 Object Segmentation in Depth Map
218(5)
13.4.1 Formation of Depth Map
218(1)
13.4.2 Density-Based in 3D Object Grouping Clustering
218(1)
13.4.3 Layered Images Object Segmentation
219(2)
13.4.3.1 Image Layer Formation
221(1)
13.4.3.2 Determination of Object Boundaries
222(1)
13.5 Conclusion
223(4)
References
224(3)
14 Real-Time Depth Estimation Using BLOB Detection/Contour Detection
227(30)
Arokia Priya Charles
Anupama V. Patil
Sunil Dambhare
14.1 Introduction
227(2)
14.2 Estimation of Depth Using Blob Detection
229(5)
14.2.1 Grayscale Conversion
230(1)
14.2.2 Thresholding
231(1)
14.2.3 Image Subtraction in Case of Input with Background
232(1)
14.2.3.1 Preliminaries
233(1)
14.2.3.2 Computing Time
234(1)
14.3 BLOB
234(7)
14.3.1 BLOB Extraction
234(1)
14.3.2 Blob Classification
235(1)
14.3.2.1 Image Moments
236(2)
14.3.2.2 Centroid Using Image Moments
238(1)
14.3.2.3 Central Moments
238(3)
14.4 Challenges
241(1)
14.5 Experimental Results
241(10)
14.6 Conclusion
251(6)
References
255(2)
Index 257
R. Arokia Priya, PhD, is Head of Electronics & Telecommunication Department at Dr. D Y Patil Institute of Engineering, Management and Research, Pune, India. She has 20 years of experience in this field as well as more than 40 publications, one patent and two copyrights to her credit.

Anupama V Patil, PhD, is the Principal at Dr. D Y Patil Institute of Engineering, Management and Research, Pune, India. She has more than 30 years of experience in this field as well as more than 40 publications and 1 patent to her credit.

Manisha Bhende, PhD, is a professor at the Marathwada Mitra Mandals Institute of Technology, Pune, India. She has 23 years of experience in this field as well as 39 research papers in international and national conferences and journals, and has published five patents and four copyrights to her credit.

Anuradha Thakare, PhD, is a professor in the Department of Computer Engineering at Pimpri Chinchwad College of Engineering, Pune, India. She has 20 years of experience in academics and research, with 78 research publications and eight IPRs (Patents and Copyrights) to her credit.

Sanjeev Wagh, PhD, is a Professor in the Department of Information Technology at Govt. College of Engineering, Karad, India. He has 71 research papers to his credit.