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
Who this book is for
Engineers, scientists, and academic researchers in areas including media processing, computational photography, video analytics, scene understanding, machine vision, face recognition, gesture recognition, pattern recognition and general object analysis.