Computational Intelligence Based Solutions for Vision Systems

PRELIMS.pdf
Preface
Acknowledgements
Editors biographies
Varun Bajaj
Irshad Ahmad Ansari
List of contributors
CH001.pdf
Chapter 1 Drone-based vision system: surveillance during calamities
1.1 Introduction
1.2 Surveillance system
1.2.1 The importance of surveillance systems
1.2.2 The use of drones in surveillance system
1.3 Proposed method
1.3.1 Detecting human faces
1.3.2 Tracking human faces
1.3.3 Locating and capturing human faces
1.3.4 Counting the number of people
1.3.5 Drone deployment and testing
1.4 Conclusion
Acknowledgements
References
CH002.pdf
Chapter 2 Use of computer vision to inspect automatically machined workpieces
2.1 Introduction
2.2 Related works
2.3 Methods
2.3.1 Image acquisition
2.3.2 Surface analysis to determine workpiece quality
2.3.3 Burr detection
2.3.4 Classification
2.4 Experimental set-up
2.5 Experimental results
2.5.1 Workpiece quality
2.5.2 Burrs
2.6 Conclusions and future work
Acknowledgements
References
CH003.pdf
Chapter 3 Machine learning for vision based crowd management
3.1 Introduction
3.2 Related work
3.2.1 A review of people count detection techniques
3.3 Proposed methodology
3.3.1 The architecture of the proposed system
3.3.2 An objective technique for counting people
3.3.3 The architecture of YOLOV3
3.4 Experimental results
3.4.1 Dataset
3.4.2 Performance analysis
3.5 Conclusion
References
CH004.pdf
Chapter 4 Skin cancer classification model based on hybrid deep feature generation and iterative mRMR
4.1 Introduction
4.1.1 Background
4.1.2 Motivation
4.1.3 Literature review
4.1.4 Our model
4.1.5 Contributions
4.1.6 Study outline
4.2 Material
4.3 Preliminary
4.3.1 Residual networks
4.3.2 DenseNet201 model
4.3.3 MobileNetV2 model
4.3.4 ShuffleNet model
4.4 The proposed framework
4.4.1 Feature generation
4.4.2 Iterative mRMR feature selector
4.4.3 Classification
4.5 Results and discussion
4.5.1 Experimental set-up
4.5.2 Results
4.5.3 Discussion
4.6 Conclusions and future works
References
CH005.pdf
Chapter 5 An analysis of human activity recognition systems and their importance in the current era
5.1 Introduction
5.2 Stages in human activity recognition
5.3 Applications of human activity recognition
5.3.1 Security video surveillance and home monitoring
5.3.2 Retail
5.3.3 Healthcare
5.3.4 Smart homes
5.3.5 Workplace monitoring
5.3.6 Entertainment
5.4 Approaches for human activity recognition
5.4.1 The HAR process using 3D posture data
5.4.2 Human action recognition using DFT
5.4.3 The local SVM approach
5.4.4 A robust approach for action recognition based on spatio-temporal features in RGB-D sequences
5.4.5 SlowFast networks for video recognition
5.4.6 Long-term recurrent convolutional networks for visual recognition and description
5.4.7 3D convolutional neural networks for human action recognition
5.4.8 Human activity recognition using an optical flow based feature set
5.4.9 Learning a hierarchical spatio-temporal model
5.4.10 Human action recognition using trajectory-based representation
5.4.11 Human activity recognition using a deep neural network with contextual information
5.5 Challenges in human activity recognition
5.5.1 Dataset
5.5.2 Sensors
5.5.3 Experimentation environment
5.5.4 Intraclass variation and interclass similarity
5.5.5 Multi-subject interactions and group activities
5.5.6 Training
5.5.7 Challenges in HAR applications
5.6 Datasets available for activity detection research
5.6.1 Action-level dataset
5.6.2 Interaction-level dataset
5.6.3 Group activities level dataset
5.6.4 Behavior-level dataset
5.7 Scope for further research in this domain
5.8 Conclusion
References
CH006.pdf
Chapter 6 A deep learning-based food detection and classification system
6.1 Introduction
6.2 Literature review
6.3 Theory
6.3.1 YOLOv3
6.3.2 YOLOv4
6.3.3 SSD
6.4 Methodology/experiments
6.4.1 Dataset
6.4.2 Data augmentation
6.4.3 Implementation
6.4.4 Software and hardware
6.4.5 Performance parameters
6.5 Results
6.6 Conclusion and future scope
References
CH007.pdf
Chapter 7 The detection of images recaptured through screenshots based on spatial rich model analysis
7.1 Introduction
7.2 Literature review
7.3 Spatial rich model
7.3.1 Computing noise residuals
7.3.2 Residual truncation and quantization
7.3.3 Formation of a sub-model with co-occurrence matrices
7.4 Proposed work
7.4.1 Selection of the neighborhood descriptor
7.5 Experimental results
7.5.1 Screenshot dataset
7.5.2 Detection performance of the neighborhood descriptors
7.5.3 The detection performance of neighborhood descriptors with an ensemble classifier
7.5.4 Detection performance of neighborhood descriptors with an SVM
7.5.5 Performance comparison of the neighborhood descriptors
7.6 Conclusion
7.7 Future work
Acknowledgements
References
CH008.pdf
Chapter 8 Data augmentation for deep ensembles in polyp segmentation
8.1 Introduction
8.2 Deep learning for semantic image segmentation
8.3 Stochastic activation selection
8.4 Data augmentation
8.4.1 Spatial stretch
8.4.2 Shadows
8.4.3 Contrast and motion blur
8.4.4 Color change and rotation
8.4.5 Segmentation
8.4.6 Rand augment
8.4.7 RICAP
8.4.8 Color and shape change
8.4.9 Occlusion 1
8.4.10 Occlusion 2
8.4.11 GridMask
8.4.12 AttentiveCutMix
8.4.13 Modified ResizeMix
8.4.14 Color mapping
8.5 Results on colorectal cancer segmentation
8.5.1 Datasets, testing protocol and metrics
8.5.2 Experiments
8.6 Conclusion
Acknowledgments
References
CH009.pdf
Chapter 9 Identification of the onset of Parkinson’s disease through a multiscale classification deep learning model utilizing a fusion of multiple conventional features with an nDS spatially exploited symmetrical convolutional pattern
9.1 Introduction
9.1.1. A comprehensive literature review
9.1.2 Contributions
9.2 Proposed methodology
9.2.1 Retrieval of voice samples
9.2.2 Pre-processing
9.2.3 Proposed multiscale multiple feature convolution with hybrid n-dilations (MMFCHnD) architecture
9.3 Experimental results and discussion
9.3.1 Evaluation metrics
9.3.2 Development of the training and testing images
9.3.3 Deep learning training details
9.3.4 Implementation results
9.4 Conclusion
References
CH010.pdf
Chapter 10 Computer vision approach with deep learning for a medical intelligence system
10.1 Introduction
10.2 Defining computer vision
10.3 Computer vision in practice
10.3.1 Medical imaging
10.3.2 Cardiology
10.3.3 Pathology
10.3.4 Dermatology
10.3.5 Ophthalmology
10.3.6 Video for medical purposes
10.3.7 The presence of humans
10.3.8 Implementation in the clinic
10.4 A case study of vision based machine learning
10.4.1 Networks of neurons
10.5 Data preparation overview
10.5.1 Data access and querying
10.5.2 De-identification
10.5.3 Data retention
10.5.4 Medical image resembling
10.5.5 Choosing an appropriate label and a definition of ground truth
10.5.6 The truth or the label’s quality
10.6 The future of computer vision and natural language processing in healthcare
10.7 Research related problems in computer vision
10.7.1 View of CNN through computer vision
10.7.2 Visualizations based on gradients
References
CH011.pdf
Chapter 11 Machine learning in medicine: diagnosis of skin cancer using a support vector machine (SVM) classifier
11.1 Introduction
11.2 Technologies used in skin cancer detection
11.3 Support vector machines (SVMs)
11.4 The SVM in skin cancer detection
11.4.1 Image acquisition
11.4.2 Feature extraction
11.4.3 SVM classification
11.5 Brief description of skin cancer detection
11.6 Challenges faced by SVMs
11.7 Future aspects in skin cancer detection
11.8 Conclusion
References