Computer Vision Metrics Survey, Taxonomy, and Analysis
epub | 11.54 MB | English | Author :Scott Krig | 978-1-4302-5929-9 | 2014 | Apress

Book 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

About the Author
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 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.

Category : | Computer Speech & Audio Processing, Document Management