Computer Vision Metrics

Survey, Taxonomy, and Analysis

by Scott Krig

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Book Description

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing 'how-to' source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

This open book is licensed under a Creative Commons License (CC BY-NC-ND). You can download Computer Vision Metrics ebook for free in PDF format (16.2 MB).

Table of Contents

Chapter 1
Image Capture and Representation
1
Chapter 2
Image Pre-Processing
39
Chapter 3
Global and Regional Features
85
Chapter 4
Local Feature Design Concepts, Classification, and Learning
131
Chapter 5
Taxonomy of Feature Description Attributes
191
Chapter 6
Interest Point Detector and Feature Descriptor Survey
217
Chapter 7
Ground Truth Data, Content, Metrics, and Analysis
283
Chapter 8
Vision Pipelines and Optimizations
313
Appendix A
Synthetic Feature Analysis
365
Appendix B
Survey of Ground Truth Datasets
401
Appendix C
Imaging and Computer Vision Resources
411
Appendix D
Extended SDM Metrics
419
Bibliography
 
437

Book Details

Subject
Computer Science
Publisher
Apress
Published
2014
Pages
498
Edition
1
Language
English
ISBN13
9781430259299
ISBN10
1430259299
ISBN13 Digital
9781430259305
ISBN10 Digital
1430259302
PDF Size
16.2 MB
License
CC BY-NC-ND

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