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. The survey is broader than it is deep, with over 540 references
provided to dig deeper. The taxonomy includes search methods, spectra
components, descriptor representation, shape, distance functions, accuracy, efficiency,
robustness and invariance attributes, and more. Rather than providing ‘how-to’ source
code examples and shortcuts, this book provides a counterpoint discussion to
the many fine opencv community source code resources available for hands-on
practitioners.
What youÂ’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, RFM, RIFF-CHOG, LGP, and more). - Shape descriptors
(Image moments, area, perimeter, centroid, D-NETS, chain codes, Fourier descriptors,
wavelets, and more) texture descriptors, structural and statistical (Harallick,
SDM, extended SDM, edge metrics, Laws metrics, RILBP, and more). - 3D
descriptors for depth-based, volumetric, and activity recognition spatio-temporal
data sets (3D HOG, HON 4D, 3D SIFT, LBP-TOP, VLBP, and more). - Basis
space descriptors (Zernike moments, KL, SLANT, steerable filter basis sets,
sparse coding, codebooks, descriptor vocabularies, and more), HAAR methods
(SURF, USURF, MUSURF, GSURF, Viola Jones, and more), descriptor-based image
reconstruction. - Distance
functions (Euclidean, SAD, SSD, correlation, Hellinger, Manhattan, Chebyshev,
EMD, Wasserstein, Mahalanobis, Bray-Curtis, Canberra, L0, Hamming, Jaccard),
coordinate spaces, robustness and invariance criteria. - Image formation,
includes CCD and CMOS sensors for 2D and 3D imaging, sensor processing topics, with
a survey identifying over fourteen (14) 3D depth sensing methods, with emphasis
on stereo, MVS, and structured light. - Image pre-processing
methods, examples are provided targeting specific feature descriptor families
(point, line and area methods, basis space methods), colorimetry (CIE, HSV,
RGB, CAM02, gamut mapping, and more). - Ground
truth data, some best-practices and examples are provided, with a survey of
real and synthetic datasets. - Vision
pipeline optimizations, mapping algorithms to compute resources (CPU, GPU,
DSP, and more), hypothetical high-level vision pipeline examples (face
recognition, object recognition, image classification, augmented reality),
optimization alternatives with consideration for performance and power to make effective
use of SIMD, VLIW, kernels, threads, parallel languages, memory, and more. - Synthetic
interest point alphabet analysis against 10 common opencv detectors to
develop intuition about how different classes of detectors