Table of contents

  1. Front Matter
    Pages i-xxxiv
  2. Scott Krig
    Pages 1-37 Open Access
  3. Scott Krig
    Pages 39-83 Open Access
  4. Scott Krig
    Pages 85-129 Open Access
  5. Scott Krig
    Pages 191-216 Open Access
  6. Scott Krig
    Pages 217-282 Open Access
  7. Scott Krig
    Pages 283-311 Open Access
  8. Scott Krig
    Pages 313-363 Open Access
  9. Scott Krig
    Pages 365-400 Open Access
  10. Scott Krig
    Pages 401-410 Open Access
  11. Scott Krig
    Pages 411-418 Open Access
  12. Scott Krig
    Pages 419-435 Open Access
  13. Scott Krig
    Pages 437-464 Open Access
  14. Back Matter
    Pages 465-472

About this book


Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. 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. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

Bibliographic information