Computer Vision Metrics

1. Front Matter

   Pages i-xxxiv

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2. Image Capture and Representation

   • Scott Krig

   Pages 1-37Open Access

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3. Image Pre-Processing

   • Scott Krig

   Pages 39-83Open Access

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4. Global and Regional Features

   • Scott Krig

   Pages 85-129Open Access

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5. Local Feature Design Concepts, Classification, and Learning

   • Scott Krig

   Pages 131-189Open Access

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6. Taxonomy of Feature Description Attributes

   • Scott Krig

   Pages 191-216Open Access

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7. Interest Point Detector and Feature Descriptor Survey

   • Scott Krig

   Pages 217-282Open Access

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

   • Scott Krig

   Pages 283-311Open Access

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9. Vision Pipelines and Optimizations

   • Scott Krig

   Pages 313-363Open Access

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10. Synthetic Feature Analysis

    • Scott Krig

    Pages 365-400Open Access

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11. Survey of Ground Truth Datasets

    • Scott Krig

    Pages 401-410Open Access

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12. Imaging and Computer Vision Resources

    • Scott Krig

    Pages 411-418Open Access

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13. Extended SDM Metrics

    • Scott Krig

    Pages 419-435Open Access

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14. Bibliography

    • Scott Krig

    Pages 437-464Open Access

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15. Back Matter

    Pages 465-472

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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.

Scott Krig is a pioneer in computer imaging, computer vision, and graphics visualization. He founded Krig Research in 1988 (krigresearch.com), providing the world’s first imaging and vision systems based onhigh-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, Scott provided direction for Intel Corporation in the area of depth-sensing and computer 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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