Deep Learning Computer Vision Project
Deep Learning Computer Vision Project
Traffic Sign Detection & Recognition
About the Book
One of the fields that have been greatly influenced by convolutional neural networks is the automotive industry. Tasks such as pedestrian detection, car detection, traffic sign recognition, traffic light recognition and road scene understanding are rarely done using hand-crafted features anymore.
Designing, implementing and evaluating are crucial steps in developing a successful computer vision-based method. In order to design a neural network, one must have the basic knowledge about the underlying process of neural network and training algorithms. Implementing a neural network requires a deep knowledge about libraries that can be used for this purpose. Moreover, neural network must be evaluated quantitatively and qualitatively before using them in practical applications.
About the Author
Bundles that include this book
Table of Contents
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Chapter 1. A Gentle Introduction to 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.
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Chapter 2. Pattern Classification
- 2.1 Formulation.
- 2.1.1 K-Nearest Neighbor.
- 2.2 Linear Classifier.
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- 2.2.1 Training a Linear Classifier.
- 2.2.2 Hinge Loss.
- 2.2.3 Logistic Regression.
- 2.3 Multiclass Classification.
- 2.3.1 One Versus One.
- 2.3.2 One Versus Rest.
- 2.3.3 Multiclass Hinge Loss.
- 2.3.4 Multinomial Logistic Function.
- 2.4 Feature Extraction.
- 2.5 Summary.
- 2.1 Formulation.
- Chapter 3. Introduction to Neural Network
- 3.1 Learning Φ(x).
- 3.2 Artificial Neural Networks.
- 3.3 Summary.
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