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
Image Capture and Representation
Global and Regional Features
Local Feature Design Concepts, Classification, and Learning
Taxonomy of Feature Description Attributes
Interest Point Detector and Feature Descriptor Survey
Ground Truth Data, Content, Metrics, and Analysis
Vision Pipelines and Optimizations
Synthetic Feature Analysis
Survey of Ground Truth Datasets
Imaging and Computer Vision Resources
Extended SDM Metrics