Computer Vision Metrics

Survey, Taxonomy, and Analysis

By Scott Krig

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Computer Vision Metrics: Survey, Taxonomy And Analysis provides a technical tour through computer vision, including history blended 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. Over 540 references are provided in the comprehensive bibliography.

Full Description

  • ISBN13: 978-1-4302-5929-9
  • 508 Pages
  • User Level: Beginner to Advanced
  • Publication Date: June 1, 2014
  • Available eBook Formats: EPUB, MOBI, PDF
  • Print Book Price: $39.99
  • eBook Price: $0.00
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Full Description

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

Who this book is for

Engineers, scientists, and academic researchers in areas including media processing, computational photography, video analytics, scene understanding, machine vision, face recognition, gesture recognition, pattern recognition and general object analysis.

Table of Contents

Table of Contents

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, Data, Metrics, and Analysis

Chapter 8. Vision Pipelines and Optimizations

Appendix A. Synthetic Feature Analysis

Appendix B. Survey of Ground Truth Datasets

Appendix C. Imaging and Computer Vision Resources

Appendix D. Extended SDM Metrics


Source Code/Downloads

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