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Computer Vision Metrics

Textbook Edition

  • Textbook
  • © 2016

Overview

Authors:
  1. Scott Krig

    1. Krig Research, USA

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  • Provides the most complete survey of computer vision feature description methods including local, regional, global, and basis feature learning via deep learning and neural networks
  • Offers learning assignments at the end of each chapter for student or instructor use
  • Includes techniques for optimizing computer vision algorithm performance such as SW and HW architecture considerations

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Image Capture and Representation

    • Scott Krig
    Pages 1-33

  3. Image Pre-Processing

    • Scott Krig
    Pages 35-74

  4. Global and Regional Features

    • Scott Krig
    Pages 75-114

  5. Local Feature Design Concepts

    • Scott Krig
    Pages 115-166

  6. Taxonomy of Feature Description Attributes

    • Scott Krig
    Pages 167-186

  7. Interest Point Detector and Feature Descriptor Survey

    • Scott Krig
    Pages 187-246

  8. Ground Truth Data, Content, Metrics, and Analysis

    • Scott Krig
    Pages 247-271

  9. Vision Pipelines and Optimizations

    • Scott Krig
    Pages 273-317

  10. Feature Learning Architecture Taxonomy and Neuroscience Background

    • Scott Krig
    Pages 319-374

  11. Feature Learning and Deep Learning Architecture Survey

    • Scott Krig
    Pages 375-514

  12. Back Matter

    Pages 515-637
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Keywords

About this book

Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. 



To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized.



The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.

Authors and Affiliations

  • Krig Research, USA

    Scott Krig

About the author

Scott Krig is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988, providing the world's first image and vision systems based on high-performance engineering workstations, super-computers, and dedicated imaging hardware, serving customers worldwide in 25 countries. Scott has provided imaging and vision solutions around the globe, and has worked closely with many industries including aerospace, military, intelligence, law enforcement, government research, and academic organizations. More recently, Scott has worked for major corporations and startups serving commercial markets, solving problems in the areas of computer vision, imaging, graphics, visualization, robotics, process control, industrial automation, computer security, cryptography, and consumer applications of imaging and machine vision to PCs, laptops, mobile phones, and tablets. Most recently, he provided direction for Intel Corporation in the area of depth-sensing andcomputer vision methods for embedded systems and mobile platforms. Scott is the author of many patent applications worldwide in the areas of embedded systems, imaging, computer vision, DRM, and computer security, and studied at Stanford.   

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