Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification / by Hamed Habibi Aghdam, Elnaz Jahani Heravi.

Format:

Book

Author/Creator:

Habibi Aghdam, Hamed, author.

Jahani Heravi, Elnaz, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Pattern perception.

Application software.

Computer organization.

Signal processing.

Image processing.

Speech processing systems.

Natural language processing (Computer science).

Automotive engineering.

Pattern Recognition.

Information Systems Applications (incl. Internet).

Computer Systems Organization and Communication Networks.

Signal, Image and Speech Processing.

Natural Language Processing (NLP).

Automotive Engineering.

Local Subjects:

Pattern Recognition.

Information Systems Applications (incl. Internet).

Computer Systems Organization and Communication Networks.

Signal, Image and Speech Processing.

Natural Language Processing (NLP).

Automotive Engineering.

Physical Description:

1 online resource (XXIII, 282 pages) : 150 illustrations, 111 illustrations in color

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

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 proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: Explains the fundamental concepts behind training linear classifiers and feature learning Discusses the wide range of loss functions for training binary and multi-class classifiers Illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks Presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks Describes two real-world examples of the detection and classification of traffic signs using deep learning methods Examines a range of varied techniques for visualizing neural networks, using a Python interface Provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Contents:

Traffic Sign Detection and Recognition

Pattern Classification

Convolutional Neural Networks

Caffe Library

Classification of Traffic Signs

Detecting Traffic Signs

Visualizing Neural Networks

Appendix A: Gradient Descend.

Other Format:

Printed edition:

ISBN:

978-3-319-57550-6

9783319575506

Access Restriction:

Restricted for use by site license.