| Description |
1 online resource (276 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Editors -- List of Contributors -- Chapter 1: Introduction:: Deep Learning and Computer Vision -- 1.1 Introduction to Deep Learning -- 1.1.1 Deep Learning -- 1.1.2 Machine Learning and Deep Learning -- 1.1.3 Types of Networks in Deep Learning -- 1.1.3.1 Connection Type of Networks -- 1.1.3.1.1 Static Feedforward Networks -- 1.1.3.1.2 Dynamic Feedback Neural Networks -- 1.1.3.2 Topology-based Neural Networks -- 1.1.3.2.1 Single-layer Neural Networks -- 1.1.3.2.2 Multilayer Neural Networks |
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1.1.3.2.3 Recurrent Neural Networks -- 1.1.3.3 Learning Methods -- 1.1.3.3.1 Supervised Learning -- 1.1.3.3.2 Unsupervised Learning -- 1.1.3.3.3 Reinforcement Learning -- 1.2 Convolutional Neural Networks -- 1.2.1 Description of Five Layers of General CNN Architecture -- 1.2.1.1 Input Layer -- 1.2.1.2 Convolutional Layer -- 1.2.1.3 Pooling Layer -- 1.2.1.4 Fully Connected Layers -- 1.2.1.5 Output Layer -- 1.2.2 Types of Architecture in CNN [ 9 ] -- 1.2.2.1 LeNet-5 -- 1.2.2.2 AlexNet -- 1.2.2.3 ZFNet -- 1.2.2.4 GoogLeNet/Inception -- 1.2.2.5 VGGNet -- 1.2.2.6 ResNet |
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1.2.3 Applications of Deep Learning -- 1.3 Image Classification, Object Detection and Face Recognition -- 1.3.1 Dataset Creation -- 1.3.2 Data Preprocessing -- 1.3.3 Image Classification -- 1.3.4 Object Detection -- 1.3.5 Face Recognition -- References -- Chapter 2: Object Detection Frameworks and Services in Computer Vision -- 2.1 Neural Networks (NNs) and Deep Neural Networks (DNNs) -- 2.1.1 Neural Networks -- 2.1.2 Single-Layer Perceptron (SLP) -- 2.1.3 Multilayer Perceptron (MLP) -- 2.2 Activation Functions -- 2.2.1 Identity Function -- 2.2.2 Sigmoid Function -- 2.2.3 Softmax Function |
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2.2.4 Tanh Function -- 2.2.5 ReLU (Rectified Linear Unit) Function -- 2.3 Loss Functions -- 2.4 Convolutional Neural Networks -- 2.4.1 CNN Architecture and its Components -- 2.5 Image Classification Using CNN -- 2.5.1 LeNet-5 -- 2.5.2 AlexNet -- 2.5.3 VGGNet -- 2.5.4 Inception and GoogLeNet -- 2.5.4.1 Inception Module -- 2.5.5 ResNet -- 2.5.5.1 Residual Block -- 2.6 Transfer Learning -- 2.6.1 Need for Transfer Learning -- 2.6.2 Transfer Learning Approaches -- 2.6.2.1 Pre-trained Network as a Classifier -- 2.6.2.2 Pre-trained Network as a Feature Extractor -- 2.6.2.3 Fine Tuning |
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2.7 Object Detection -- 2.7.1 Object Localization -- 2.7.1.1 Sliding Window Detection -- 2.7.1.2 Bounding Box Prediction -- 2.7.2 Components of Object Detection Frameworks -- 2.8 Region-Based Convolutional Neural Networks (R-CNNs) -- 2.8.1 R-CNN -- 2.8.2 Fast R-CNN -- 2.8.2.1 Components of Fast R-CNN -- 2.8.3 Faster R-CNN -- 2.8.4 YOLO Algorithm -- 2.8.5 YOLOv1 Object Detection Model -- 2.8.6 YOLO9000 Object Detection Model -- 2.8.7 YOLOv3 Object Detection Model -- 2.9 Computer Vision Application Areas -- References |
| Notes |
Description based upon print version of record |
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Chapter 3: Real-Time Tracing and Alerting System for Vehicles and Children to Ensure Safety and Security, Using LabVIEW |
| Form |
Electronic book
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| Author |
Kumar Dhanraj, Rajesh
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Balusamy, Balamurugan
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| ISBN |
9781000686746 |
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1000686744 |
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