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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.
List of contents
Preface xiii
1 Data Conditioning for Medical Imaging 1
Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi
1.1 Introduction 2
1.2 Importance of Image Preprocessing 2
1.3 Introduction to Digital Medical Imaging 3
1.3.1 Types of Medical Images for Screening 4
1.3.1.1 X-rays 4
1.3.1.2 Computed Tomography (CT) Scan 4
1.3.1.3 Ultrasound 4
1.3.1.4 Magnetic Resonance Imaging (MRI) 5
1.3.1.5 Positron Emission Tomography (PET) Scan 5
1.3.1.6 Mammogram 5
1.3.1.7 Fluoroscopy 5
1.3.1.8 Infrared Thermography 6
1.4 Preprocessing Techniques of Medical Imaging Using Python 6
1.4.1 Medical Image Preprocessing 6
1.4.1.1 Reading the Image 7
1.4.1.2 Resizing the Image 7
1.4.1.3 Noise Removal 8
1.4.1.4 Filtering and Smoothing 9
1.4.1.5 Image Segmentation 11
1.5 Medical Image Processing Using Python 13
1.5.1 Medical Image Processing Methods 16
1.5.1.1 Image Formation 17
1.5.1.2 Image Enhancement 19
1.5.1.3 Image Analysis 19
1.5.1.4 Image Visualization 19
1.5.1.5 Image Management 19
1.6 Feature Extraction Using Python 20
1.7 Case Study on Throat Cancer 24
1.7.1 Introduction 24
1.7.1.1 HSI System 25
1.7.1.2 The Adaptive Deep Learning Method Proposed 25
1.7.2 Results and Findings 27
1.7.3 Discussion 28
1.7.4 Conclusion 29
1.8 Conclusion 29
References 30
Additional Reading 31
Key Terms and Definition 32
2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33
Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar
2.1 Introduction 33
2.2 Literature Review 35
2.3 Learning Methods 41
2.3.1 Machine Learning 41
2.3.2 Deep Learning 42
2.3.3 Transfer Learning 42
2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43
2.4.1 Dataset Description 43
2.4.2 Evaluation Platform 44
2.4.3 Training Process 44
2.4.4 Model Evaluation of CNN Classifier 46
2.4.5 Mathematical Model 47
2.4.6 Parameter Optimization 47
2.4.7 Performance Metrics 50
2.5 Conclusion 52
References 53
3 Contamination Monitoring System Using IOT and GIS 57
Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari
3.1 Introduction 58
3.2 Literature Survey 58
3.3 Proposed Work 60
3.4 Experimentation and Results 61
3.4.1 Experimental Setup 61
3.5 Results 64
3.6 Conclusion 70
Acknowledgement 71
References 71
4 Video Error Concealment Using Particle Swarm Optimization 73
Rajani P. K. and Arti Khaparde
4.1 Introduction 74
4.2 Proposed Research Work Overview 75
4.3 Error Detection 75
4.4 Frame Replacement Video Error Concealment Algorithm 77
4.5 Research Methodology 77
4.5.1 Particle Swarm Optimization 78
4.5.2 Spatio-Temporal Video Error Concealment Method 78
4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79
4.6 Results and Analysis 83
4.6.1 Single Frame With Block Error Analysis 85
4.6.2 Single Frame With Random Error Analysis 86
4.6.3 Multiple Frame Error Analysis 88
4.6.4 Seq
About the author
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 IPR's (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.
Summary
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.
