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Computer Vision Metrics Survey, Taxonomy, and Analysis by Scott Krig is a science book available to read on EtoBox.

What is Computer Vision Metrics Survey, Taxonomy, and Analysis about?

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 What you’ll learn Interest point & descriptor concepts (interest points, corners, ridges, blobs, contours, edges, maxima), interest point tuning and culling, interest point methods (Laplacian, LOG, Moravic, Harris, Harris-Stephens, Shi-Tomasi, Hessian, difference of Gaussians, salient regions, MSER, SUSAN, FAST, FASTER, AGHAST, local curvature, morphological regions, and more), descriptor concepts (shape, sampling pattern, spectra, gradients, binary patterns, basis features), feature descriptor families. Local binary descriptors (LBP, LTP, FREAK, ORB, BRISK, BRIEF, CENSUS, and more). Gradient descriptors (SIFT, SIFT-PCA, SIFT-SIFER, SIFT-GLOH, Root SIFT, CensureE, STAR, HOG, PHOG, DAISY, O-DAISY, CARD

Who reads Computer Vision Metrics Survey, Taxonomy, and Analysis?

Author

Scott Krig

Publisher

APRESS open

Published

2014

Language

EN

Category

science

Subjects

Computer Science, Stem

Rating

4.4 / 5 (69 ratings)

Updated

2026-03-14