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Computer Vision Metrics: Introduction

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Dirt. This is a jar of dirt.
...Is the jar of dirt going to help? If you don’t want it, give it back.
—Pirates Of The Carribean, Jack Sparrow and Tia Dalma

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. The focus is on the pixel-side of the vision pipeline, rather than the back-end training, classification, machine learning and matching stages. This book is suitable for reference, higher-level courses, and self-directed study in computer vision. The book is aimed at someone already familiar with computer vision and image processing; however, even those new to the field will find good introductions to the key concepts at a high level, via the ample illustrations and summary tables.

I view computer vision as a mathematical artform and its researchers and practitioners as artists. So, this book is more like a tour through an art gallery rather than a technical or scientific treatise. Observations are provided, interesting questions are raised, a vision taxonomy is suggested to draw a conceptual map of the field, and references are provided to dig deeper. This book is like an attempt to draw a map of the world centered around feature metrics, inaccurate and fuzzy as the map may be, with the hope that others will be inspired to expand the level of detail in their own way, better than what I, or even a few people, can accomplish alone. If I could have found a similar book covering this particular slice of subject matter, I would not have taken on the project to write this book.

What is not in the Book

Readers looking for computer vision “‘how-to”’ source code examples, tutorial discussions, performance analysis, and short-cuts will not find them here, and instead should consult the well-regarded library resources, including many fine books, online resources, source code examples, and several blogs. There is nothing better than OpenCV for the hands-on practitioner. For this reason, this book steers a clear path around duplication of the “how-to” materials already provided by the OpenCV community and elsewhere, and instead provides a counterpoint discussion, including a comprehensive survey, analysis and taxonomy of methods. Also, do not expect all computer vision topics to be covered deeply with proofs and performance analysis, since the bibliography references cover these matters quite well: for example, machine learning, training and classification methods are only lightly introduced, since the focus here is on the feature metrics.

In summary, this book is about the feature metrics, showing “‘what”’ methods practitioners are using, with detailed observations and analysis of...