Intro -- Preface -- Acknowledgements -- Editors biographies -- Varun Bajaj -- Irshad Ahmad Ansari -- List of contributors -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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.