- Full Description
Computer Vision Metrics provides an extensive survey and analysis of current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. 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. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, and observations about tuning the methods for achieving robustness and invariance targets for specific applications. This is not another ‘how-to’ book with source code examples and shortcuts, but rather provides a counterpoint to the many fine opencv community source code resources currently available for hands-on practitioners.
What youll learn
Here is a sampling of topics covered:
- 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, more), descriptor concepts (shape, sampling pattern, spectra, gradients, binary patterns, basis features).
- Local binary descriptors (LBP, LBP-TOP, LTP, FREAK, ORB, BRISK, BRIEF, CENSUS, more).
- Gradient descriptors (SIFT, SIFT-PAC, SIFT-SIFER, SIFT-GLOH, Root SIFT, CensureE,, STAR, HOG, PHOG, DAISY, O-DAISY, CARD, RFM, RIFF-CHOG, LGP, more).
- Shape descriptors (Image Moments, area, perimeter, centroid, D-NETS, Chain Codes, Fourier, Wavelets, more) texture descriptors, structural and statistical (Harallick, SDM, extended SDM, edge metrics, Laws Metrics, LBP, more).
- 3D descriptors (3D HOG, HON 4D, 3D SIFT, more).
- Basis space descriptors (Zernike Moments, several other transforms, Steerable Filter Basis Sets, Fourier Descriptors, Sparse Coding, Codebooks, Descriptor Vocabularies, more), HAAR methods (SURF, USURF, MUSURF, GSURF, Viola Jones, more), descriptor-based image reconstruction.
- Image formation, includes CCD and CMOS sensors for 2D and 3D imaging, along with sensor processing topics, with a survey identifying over fourteen (14) 3D depth sensing methods, with emphasis on stereo, MVS, and structured light.
- Image-processing methods targeted at specific feature descriptor methods (point, line and area methods, basis space methods), colorimetry (CIE, HSV, RGB, CAM02, gamut mapping, more).
- Distance functions (Euclidean, Cartesian, SAD, SSD, Correlation, Hellinger, Manhattan, Chebyshev, EMD, Wasserstein, Mahalanobis, Bray-Curtis, Canberra, L0, Hamming, Jaccard), coordinate spaces, robustness and invariance criteria, light introduction to machine learning, classification and training (SVM,’s kernel machines, KNN, convolutional networks, neural networks, more).
- Ground truth data, real and synthetic datasets, survey of datasets.
- Vision pipeline optimizations, types of compute resources (CPU, GPU, DSP, more), hypothetical optimized vision pipeline examples (face recognition, object recognition, image classification, augmented reality), optimization alternatives for effective use of SIMD, VLIW, kernels, threads, parallel languages, memory, more.
- Synthetic interest point alphabet analysis against 10 common OpenCV detectors to develop intuition about how different classes of detectors actually work (SIFT, SURF, BRISK, FAST, HARRIS, GFFT, MSER, ORB, STAR, SIMPLEBLOB). Source code provided online.
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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