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Guide to convolutional neural networks : a practical application to traffic-sign detection and classification

Author: Hamed Habibi Aghdam; Elnaz Jahani Heravi
Publisher: Cham, Switzerland : Springer, 2017.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Hamed Habibi Aghdam; Elnaz Jahani Heravi
ISBN: 9783319575506 3319575503
OCLC Number: 987790957
Description: 1 online resource (xxiii, 282 pages) : illustrations (some color)
Contents: Preface; Books Website; Contents; Acronyms; List of Figures; 1 Traffic Sign Detection and Recognition; 1.1 Introduction; 1.2 Challenges; 1.3 Previous Work; 1.3.1 Template Matching; 1.3.2 Hand-Crafted Features; 1.3.3 Feature Learning; 1.3.4 ConvNets; 1.4 Summary; 2 Pattern Classification; 2.1 Formulation; 2.1.1 K-Nearest Neighbor; 2.2 Linear Classifier; 2.2.1 Training a Linear Classifier; 2.2.2 Hinge Loss; 2.2.3 Logistic Regression; 2.2.4 Comparing Loss Function; 2.3 Multiclass Classification; 2.3.1 One Versus One; 2.3.2 One Versus Rest; 2.3.3 Multiclass Hinge Loss 3.5.2 Software Libraries3.5.3 Evaluating a ConvNet; 3.6 Training a ConvNet; 3.6.1 Loss Function; 3.6.2 Initialization; 3.6.3 Regularization; 3.6.4 Learning Rate Annealing; 3.7 Analyzing Quantitative Results; 3.8 Other Types of Layers; 3.8.1 Local Response Normalization; 3.8.2 Spatial Pyramid Pooling; 3.8.3 Mixed Pooling; 3.8.4 Batch Normalization; 3.9 Summary; 3.10 Exercises; 4 Caffe Library; 4.1 Introduction; 4.2 Installing Caffe; 4.3 Designing Using Text Files; 4.3.1 Providing Data; 4.3.2 Convolution Layers; 4.3.3 Initializing Parameters; 4.3.4 Activation Layer; 4.3.5 Pooling Layer 4.3.6 Fully Connected Layer4.3.7 Dropout Layer; 4.3.8 Classification and Loss Layers; 4.4 Training a Network; 4.5 Designing in Python; 4.6 Drawing Architecture of Network; 4.7 Training Using Python; 4.8 Evaluating Using Python; 4.9 Save and Restore Networks; 4.10 Python Layer in Caffe; 4.11 Summary; 4.12 Exercises; 5 Classification of Traffic Signs; 5.1 Introduction; 5.2 Related Work; 5.2.1 Template Matching; 5.2.2 Hand-Crafted Features; 5.2.3 Sparse Coding; 5.2.4 Discussion; 5.2.5 ConvNets; 5.3 Preparing Dataset; 5.3.1 Splitting Data; 5.3.2 Augmenting Dataset 5.3.3 Static Versus One-the-Fly Augmenting5.3.4 Imbalanced Dataset; 5.3.5 Preparing the GTSRB Dataset; 5.4 Analyzing Training/Validation Curves; 5.5 ConvNets for Classification of Traffic Signs; 5.6 Ensemble of ConvNets; 5.6.1 Combining Models; 5.6.2 Training Different Models; 5.6.3 Creating Ensemble; 5.7 Evaluating Networks; 5.7.1 Misclassified Images; 5.7.2 Cross-Dataset Analysis and Transfer Learning; 5.7.3 Stability of ConvNet; 5.7.4 Analyzing by Visualization; 5.8 Analyzing by Visualizing; 5.8.1 Visualizing Sensitivity; 5.8.2 Visualizing the Minimum Perception
Responsibility: Hamed Habibi Aghdam, Elnaz Jahani Heravi.

Abstract:

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of  Read more...

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