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Computer Vision Metrics

Survey, Taxonomy, and Analysis

Scott Krig (Autor)

Buch | Hardcover

x, 740 Seiten

2016 | 2nd ed. 2016
Springer International Publishing (Verlag)
978-3-319-30899-9 (ISBN)

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This new textbook edition provides a comprehensive history and state-of-the-art survey for fundamental computer vision methods. Expanded and updated, this book features over 300 new references, totaling over 800 in all, as well as learning assignments at the end of each chapter to help students and researchers dig deeper into key topics. This survey covers everything from imaging devices, computational imaging, interest point detectors, local 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 book includes useful analysis to provide intuition into the goals of various methods, why they work, and how they may be optimized. This is not a how-to book with source code examples, but rather a survey and taxonomy intended as a reference tool for researchers and engineers, complimenting the many fine hand-on resources and open source projects such as OpenCV and other imaging and deep learning tools.

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, 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.

Image Capture and Representation.- Image Re-processing.- Global and Regional Features.- Local Feature Design Concepts.- Taxonomy of Feature Description Attributes.- Interest Point Detector and Feature Descriptor Survey.- Ground Truth Data, Content, Metrics, and Analysis.- Vision Pipeline and Optimizations.- Feature Learning Architecture Taxonomy and Neuroscience Background.- Feature Learning and Deep Learning Architecture Survey.

Zusatzinfo	X, 740 p. 100 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik
Schlagworte	3D Reconstruction • CNN • CNN (Cable News Network) • computational imaging • Computational Neuroscience • Computer Science • computer vision • convolutional neural networks • Deep learning • deep neural networks • DNN • Document Preparation and Text Processing • Feature Descriptors • Feature learning • Image Processing • image processing and computer vision • Neural networks • Signal, Image and Speech Processing
ISBN-10	3-319-30899-8 / 3319308998
ISBN-13	978-3-319-30899-9 / 9783319308999
Zustand	Neuware