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BOOKS FOR PROFESSIONALS BY PROFESSIONALS
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Computer Vision Metrics
Computer Vision Metrics: Survey, Taxonomy, and Analysis provides a technical tour
through computer vision, with a survey of nearly 100 types of local, regional, and
global feature descriptors, blending history of the field with state-of-the-art analysis
of contemporary methods, rather than just another how-to book with source code
shortcuts and performance analysis. 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 to the literature 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. Several 3D depth sensing methods are surveyed including MVS, stereo, and
structured light.
This work focuses on a slice through the field 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,
with a light introduction to the back-end training, classification, machine learning, and
matching stages.
Computer Vision Metrics is written for engineers, scientists, and academic
researchers in areas including video analytics, scene understanding, machine vision,
face recognition, gesture recognition, pattern recognition, general object analysis, media
processing, and computational photography.
What You’ll Learn:
• Current status, brief history, and future directions for computer vision metrics
• Taxonomy of local binary, gradient & other spectra, shape features,
and basis spaces
• Overview of 2D image sensing, 3D depth sensing, and image preprocessing
• Vision pipeline optimization methods for computer vision applications
• Characterization of ten OpenCV detectors using synthetic feature alphabets
9781430 259299
53999
ISBN 978-1-4302-5929-9
Krig
For your convenience Apress has placed some of the front
matter material after the index. Please use the Bookmarks
and Contents at a Glance links to access them.
v
Contents at a Glance
About the Author ��������������������������������������������������������������������������xxvii
Acknowledgments �������������������������������������������������������������������������xxix
Introduction �����������������������������������������������������������������������������������xxxi
Chapter 1: Image Capture and Representation ■ ������������������������������� 1
Chapter 2: Image Pre-Processing ■ ������������������������������������������������� 39
Chapter 3: Global and Regional Features ■ ������������������������������������� 85
Chapter 4: Local Feature Design Concepts, Classification, ■
and Learning ������������������������������������������������������������������������������� 131
Chapter 5: Taxonomy of Feature Description Attributes ■ ������������� 191
Chapter 6: Interest Point Detector and Feature ■
Descriptor Survey ����������������������������������������������������������������������� 217
Chapter 7: Ground Truth Data, Content, Metrics, and Analysis ■ ��� 283
Chapter 8: Vision Pipelines and Optimizations ■ ��������������������������� 313
Appendix A: Synthetic Feature Analysis ■ ������������������������������������� 365
Appendix B: Survey of Ground Truth Datasets ■ ���������������������������� 401
Appendix C: Imaging and Computer Vision Resources ■ ��������������� 411
Appendix D: Extended SDM Metrics ■ ������������������������������������������� 419
Bibliography ■ ������������������������������������������������������������������������������� 437
Index ���������������������������������������������������������������������������������������������� 465
xxxi
Introduction
Dirt. is is a jar of dirt.
Yes.
. . . Is the jar of dirt going to help?
If you don’t want it, give it back.
—Pirates Of e Carribean, Jack Sparrow and Tia Dalma
is work focuses on a slice through the eld - 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. e focus is on the pixel-side
of the vision pipeline, rather than the back-end training, classication, machine learning
and matching stages. is book is suitable for reference, higher-level courses, and
self-directed study in computer vision. e book is aimed at someone already familiar
with computer vision and image processing; however, even those new to the eld will
nd 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 scientic treatise. Observations are provided, interesting questions are raised,
a vision taxonomy is suggested to draw a conceptual map of the eld, and references are
provided to dig deeper. is 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 nd them here, and instead
should consult the well-regarded
http://opencv.org library resources, including many
ne books, online resources, source code examples, and several blogs. ere 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,
■ IntRODUCtIOn
xxxii
since the bibliography references cover these matters quite well: for example, machine
learning, training and classication 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 “‘why”’ those methods
work, with a bias towards raising questions via observations rather than providing too
many answers. I like the questions best because good questions lead to many good
answers, and each answer is often pregnant with more good questions...
is book is aimed at a survey level, with a taxonomy and analysis, so no detailed
examples of individual use-cases or horse races between methods are included. However,
much detail is provided in over 540+ bibliographic references to dig deeper into practical
matters. Additionally, some “‘how-to”’ and “‘hands-on”’ resources are provided in
Appendix C. And a little ‘perfunctory’ source code accompanying parts of this book is
available online, for Appendix A covering the interest point detector evaluations for
the synthetic interest point alphabets introduced in Chapter 7; and in Appendix D for
extended SDM metrics covered in Chapter 3.
What is in the Book
Specically, Chapter 1 provides preamble on 2d image formation and 3d depth imaging,
and Chapter 2 promotes intelligent image pre-processing to enhance feature description.
Chapters 3 through 6 form the core discussion on feature description, with an emphasis
on local features. Global and regional metrics are covered in Chapter 3, feature descriptor
concepts in Chapter 4, a vision taxonomy is suggested in Chapter 5, and local feature
description is covered in Chapter 6. Ground truth data is covered in Chapter 7, and
Chapter 8 discusses hypothetical vision pipelines and hypothetical optimizations from
an engineering perspective, as a set of exercises to tie vision concepts together into
real systems (coursework assignments can be designed to implement and improve
the hypothetical examples in Chapter 8). A set of synthetic interest point alphabets is
developed in Chapter 7, and ten common detectors are run against those alphabets, with
the results provided in Appendix A. It is dicult to cleanly partition all topics in image
processing and computer vision, so there is some overlap in the chapters. Also, there
are many hybrids used in practice, so there’s inevitable overlap in the Chapter 5 vision
taxonomy, and creativity always arrives on the horizon to nd new and unexpected ways
of using old methods. However, the taxonomy is a starting point and helped to guide the
organization of the book.
erefore, the main goal has been to survey and understand the range of methods
used to describe features, without passing judgment on which methods are better.
Some history is presented to describe why certain methods were developed, and what
properties of invariance or performance were the goals, and we leave the claims to be
proven by others, since “how” each method is implemented determines performance
and accuracy, and “what” each method is tested against in terms of ground truth data
really tells the rest of the story. If we can glean good ideas from the work of others, that is
a measure of the success of their work.
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