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Computer Vision Metrics: Survey, Taxonomy, and Analysis
 
 

Computer Vision Metrics: Survey, Taxonomy, and Analysis [Kindle Edition]

Scott Krig

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Product Description

Product Description

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 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, 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

Product details

  • Format: Kindle Edition
  • File Size: 12349 KB
  • Print Length: 510 pages
  • Page Numbers Source ISBN: 1430259299
  • Publisher: Apress; 1 edition (22 May 2014)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00K6N4JS0
  • Text-to-Speech: Enabled
  • X-Ray:
  • Amazon Bestsellers Rank: #8,908 Free in Kindle Store (See Top 100 Free in Kindle Store)
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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 5.0 out of 5 stars  3 reviews
1 of 1 people found the following review helpful
5.0 out of 5 stars Comprehensive book, exactly as advertised 26 Jun 2014
By SNT - Published on Amazon.com
Format:Paperback
I acquired this book recently as a reference for various computer vision techniques or algorithms. As a software architect / image processing professional, I find it extremely useful to have a survey this broad on my shelf. It covers literally hundreds of algorithms in a sensible, organized way.

I hope he updates this every few years to keep up with each new generation of CV algorithms.
5.0 out of 5 stars Holy moley. Amazing reference. 3 Aug 2014
By Epilady - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
Wow. What a good reference covering
Image Capture and Representation
Image Pre-Processing
Global and Regional Features
Local Feature Design Concepts, Classification, and Learning
Taxonomy of Feature Description Attributes
Interest Point Detector and Feature Descriptor Survey
Ground Truth Data, Content, Metrics and Analysis (my main interest in this book)
Vision Pipelines and Optimizations

Literally 100s of algorithms are included and the books is well organized and as easy to read as this topic could probably be.

Excellent, excellent reference.
5.0 out of 5 stars Helped me out, and price is ridiculously right 27 July 2014
By Eric Haines - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
From my pretty limited knowledge of image processing, this looks like a useful survey book, running through common techniques and pointing to relevant references. Me, I was interested in segmentation algorithms for non-photorealistic rendering, and it has a reasonable section all about this topic. At a price of "free", this automatically kicks it up +1 star for 5 stars.

Note that the (also good) book “Computer Vision: Algorithms and Applications” is also free for download as a PDF.
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