Computer Vision Metrics: Survey, Taxonomy, and Analysis

Computer Vision Metrics: Survey, Taxonomy, and Analysis

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.


Chapter 1:​ Image Capture and Representation
Chapter 2:​ Image Pre-Processing
Chapter 3:​ Global and Regional Features
Chapter 4:​ Local Feature Design Concepts, Classification, and Learning
Chapter 5:​ Taxonomy of Feature Description Attributes
Chapter 6:​ Interest Point Detector and Feature Descriptor Survey
Chapter 7:​ Ground Truth Data, Content, Metrics, and Analysis
Chapter 8:​ Vision Pipelines and Optimizations

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