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Computer Vision Metrics provides an extensive survey and analysis of current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. 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. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, and observations about tuning the methods for achieving robustness and invariance targets for specific applications. This is not another ‘how-to’ book with source code examples and shortcuts, but rather provides a counterpoint to the many fine opencv community source code resources currently available for hands-on practitioners.