Computer Vision Metrics: Survey, Taxonomy, And Analysis provides a technical tour through computer vision, with a survey of over 70 local feature descriptors, blending history of the field with state-of-the-art analysis of contemporary methods, rather than just another 'how-to' book with lots of source code. Observations are provided to develop intuition behind the methods and mathematics, interesting questions are raised for future research rather than providing all the answers, and a Vision Taxonomy is suggested to draw a conceptual map of the field. Extensive illustrations are included, with over 540 references in the comprehensive bibliography to dig deeper. Computer Vision Metrics explores the key questions behind the design and mathematics of computer vision metrics and feature descriptors, providing a comprehensive survey and taxonomy of 'what' methods are used, with analysis and observations about 'why' the methods work. This work focuses on a slice through the field-Computer Vision Metrics-from the view of feature description metrics, or how to describe, compute and design the macro-features and micro-features that make up larger objects in images. Nearly 100 types of global, regional and local features are surveyed. The focus is on the pixel-side of the vision pipeline, rather than the back-end training, classification, machine learning and matching stages. Computer Vision Metrics is not another 'how-to' book with shortcuts, source code examples, and performance analysis, but rather fills a gap in the literature to analyze a wide range of key feature descriptors such as SIFT, SURF, D-NETS, ORB, FREAK, basis spaces, polygon shape descriptors and many other methods, providing a counterpoint discussion intended to compliment the flourishing OpenCV community resources, which already provide ample tutorials and 'how-to' sample code.