- Full 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-featuresComputer 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.
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.
What youll 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.
- Table of Contents
Table of Contents
Chapter 1. Image Capture and Representation
Chapter 2. Image Pre-Processing
Chapter 3. Global and Regional Features
Chapter 4. Local Feature Design Concepts, Classification, and Learning
Chapter 5. Taxonomy Of Feature Description Attributes
Chapter 6. Interest Point Detector and Feature Descriptor Survey
Chapter 7. Ground Truth Data, Data, Metrics, and Analysis
Chapter 8. Vision Pipelines and Optimizations
Appendix A. Synthetic Feature Analysis
Appendix B. Survey of Ground Truth Datasets
Appendix C. Imaging and Computer Vision Resources
Appendix D. Extended SDM Metrics
- Source Code/Downloads
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