Computer Vision Metrics : Textbook Edition / by Scott Krig.

Format:

Book

Author/Creator:

Krig, Scott, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Signal processing.

Image processing.

Speech processing systems.

Computational intelligence.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Signal, Image and Speech Processing.

Computational Intelligence.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Signal, Image and Speech Processing.

Computational Intelligence.

Physical Description:

1 online resource (XVIII, 637 pages) : 331 illustrations, 139 illustrations in color

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.

Contents:

Image Capture and Representation

Image Re-processing

Global and Regional Features

Local Feature Design Concepts

Taxonomy of Feature Description Attributes

Interest Point Detector and Feature Descriptor Survey

Ground Truth Data, Content, Metrics, and Analysis

Vision Pipeline and Optimizations

Feature Learning Architecture Taxonomy and Neuroscience Background

Feature Learning and Deep Learning Architecture Survey. .

Other Format:

Printed edition:

ISBN:

978-3-319-33762-3

9783319337623

Access Restriction:

Restricted for use by site license.