| 000 | 10866nam a22004333i 4500 | ||
|---|---|---|---|
| 001 | EBC6422722 | ||
| 003 | MiAaPQ | ||
| 005 | 20220623112335.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 220617s2014 xx o ||||0 eng d | ||
| 020 |
_a9781430259305 _q(electronic bk.) |
||
| 020 | _z9781430259299 | ||
| 035 | _a(MiAaPQ)EBC6422722 | ||
| 035 | _a(Au-PeEL)EBL6422722 | ||
| 035 | _a(OCoLC)881519747 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
||
| 050 | 4 | _aT385 | |
| 100 | 1 | _aKrig, Scott. | |
| 245 | 1 | 0 |
_aComputer Vision Metrics : _bSurvey, Taxonomy, and Analysis. |
| 264 | 1 |
_aBerkeley, CA : _bApress L. P., _c2014. |
|
| 264 | 4 | _c�2014. | |
| 300 | _a1 online resource (498 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 505 | 0 | _aIntro -- Contents at a Glance -- Contents -- About the Author -- Acknowledgments -- Introduction -- Chapter 1: Image Capture and Representation -- Image Sensor Technology -- Sensor Materials -- Sensor Photo-Diode Cells -- Sensor Configurations: Mosaic, Foveon, BSI -- Dynamic Range and Noise -- Sensor Processing -- De-Mosaicking -- Dead Pixel Correction -- Color and Lighting Corrections -- Geometric Corrections -- Cameras and Computational Imaging -- Overview of Computational Imaging -- Single-Pixel Computational Cameras -- 2D Computational Cameras -- 3D Depth Camera Systems -- Binocular Stereo -- Structured and Coded Light -- Optical Coding: Diffraction Gratings -- Time-of-Flight Sensors -- Array Cameras -- Radial Cameras -- Plenoptics: Light Field Cameras -- 3D Depth Processing -- Overview of Methods -- Problems in Depth Sensing and Processing -- The Geometric Field and Distortions -- The Horopter Region, Panum's Area, and Depth Fusion -- Cartesian vs. Polar Coordinates: Spherical Projective Geometry -- Depth Granularity -- Correspondence -- Holes and Occlusion -- Surface Reconstruction and Fusion -- Noise -- Monocular Depth Processing -- Multi-View Stereo -- Sparse Methods: PTAM -- Dense Methods: DTAM -- Optical Flow, SLAM, and SFM -- 3D Representations: Voxels, Depth Maps, Meshes, and Point Clouds -- Summary -- Chapter 2: Image Pre-Processing -- Perspectives on Image Processing -- Problems to Solve During Image Pre-Processing -- Vision Pipelines and Image Pre-Processing -- Corrections -- Enhancements -- Preparing Images for Feature Extraction -- Local Binary Family Pre-Processing -- Spectra Family Pre-Processing -- Basis Space Family Pre-Processing -- Polygon Shape Family Pre-Processing -- The Taxonomy of Image Processing Methods -- Point -- Line -- Area -- Algorithmic -- Data Conversions -- Colorimetry -- Overview of Color Management Systems. | |
| 505 | 8 | _aIlluminants, White Point, Black Point, and Neutral Axis -- Device Color Models -- Color Spaces and Color Perception -- Gamut Mapping and Rendering Intent -- Practical Considerations for Color Enhancements -- Color Accuracy and Precision -- Spatial Filtering -- Convolutional Filtering and Detection -- Kernel Filtering and Shape Selection -- Shape Selection or Forming Kernels -- Point Filtering -- Noise and Artifact Filtering -- Integral Images and Box Filters -- Edge Detectors -- Kernel Sets: Sobel, Scharr, Prewitt, Roberts, Kirsch, Robinson, and Frei-Chen -- Canny Detector -- Transform Filtering, Fourier, and Others -- Fourier Transform Family -- Fundamentals -- Fourier Family of Transforms -- Other Transforms -- Morphology and Segmentation -- Binary Morphology -- Gray Scale and Color Morphology -- Morphology Optimizations and Refinements -- Euclidean Distance Maps -- Super-Pixel Segmentation -- Graph-based Super-Pixel Methods -- Gradient-Ascent-Based Super-Pixel Methods -- Depth Segmentation -- Color Segmentation -- Thresholding -- Global Thresholding -- Histogram Peaks and Valleys, and Hysteresis Thresholds -- LUT Transforms, Contrast Remapping -- Histogram Equalization and Specification -- Global Auto Thresholding -- Local Thresholding -- Local Histogram Equalization -- Integral Image Contrast Filters -- Local Auto Threshold Methods -- Chapter 3: Global and Regional Features -- Historical Survey of Features -- Key Ideas: Global, Regional, and Local -- 1960s, 1970s, 1980s-Whole-Object Approaches -- Early 1990s-Partial-Object Approaches -- Mid-1990s-Local Feature Approaches -- Late 1990s-Classified Invariant Local Feature Approaches -- Early 2000s-Scene and Object Modeling Approaches -- Mid-2000s-Finer-Grain Feature and Metric Composition Approaches -- Post-2010-Multi-Modal Feature Metrics Fusion -- Textural Analysis. | |
| 505 | 8 | _a1950s thru 1970s-Global Uniform Texture Metrics -- 1980s-Structural and Model-Based Approaches for Texture Classification -- 1990s-Optimizations and Refinements to Texture Metrics -- 2000 toToday-More Robust Invariant Texture Metrics and 3D Texture -- Statistical Methods -- Texture Region Metrics -- Edge Metrics -- Edge Density -- Edge Contrast -- Edge Entropy -- Edge Directivity -- Edge Linearity -- Edge Periodicity -- Edge Size -- Edge Primitive Length Total -- Cross-Correlation and Auto-Correlation -- Fourier Spectrum, Wavelets, and Basis Signatures -- Co-Occurrence Matrix, Haralick Features -- Extended SDM Metrics -- Metric 1: Centroid -- Metric 2: Total Coverage -- Metric 3: Low-Frequency Coverage -- Metric 4: Corrected Coverage -- Metric 5: Total Power -- Metric 6: Relative Power -- Metric 7: Locus Mean Density -- Metric 8: Locus Length -- Metric 9: Bin Mean Density -- Metric 10: Containment -- Metric 11. Linearity -- Metric 12: Linearity Strength -- Laws Texture Metrics -- LBP Local Binary Patterns -- Dynamic Textures -- Statistical Region Metrics -- Image Moment Features -- Point Metric Features -- Global Histograms -- Local Region Histograms -- Scatter Diagrams, 3D Histograms -- Multi-Resolution, Multi-Scale Histograms -- Radial Histograms -- Contour or Edge Histograms -- Basis Space Metrics -- Fourier Description -- Walsh-Hadamard Transform -- HAAR Transform -- Slant Transform -- Zernike Polynomials -- Steerable Filters -- Karhunen-Loeve Transform and Hotelling Transform -- Wavelet Transform and Gabor Filters -- Gabor Functions -- Hough Transform and Radon Transform -- Summary -- Chapter 4: Local Feature Design Concepts, Classification, and Learning -- Local Features -- Detectors, Interest Points, Keypoints, Anchor Points, Landmarks -- Descriptors, Feature Description, Feature Extraction -- Sparse Local Pattern Methods. | |
| 505 | 8 | _aLocal Feature Attributes -- Choosing Feature Descriptors and Interest Points -- Feature Descriptors and Feature Matching -- Criteria for Goodness -- Repeatability, Easy vs. Hard to Find -- Distinctive vs. Indistinctive -- Relative and Absolute Position -- Matching Cost and Correspondence -- Distance Functions -- Early Work on Distance Functions -- Euclidean or Cartesian Distance Metrics -- Euclidean Distance -- Squared Euclidean Distance -- Cosine Distance or Similarity -- Sum of Absolute Differences (SAD) or L1 Norm -- Sum of Squared Differences (SSD) or L2 Norm -- Correlation Distance -- Hellinger Distance -- Grid Distance Metrics -- Manhattan Distance -- Chebyshev Distance -- Statistical Difference Metrics -- Earth Movers Distance (EMD) or Wasserstein Metric -- Mahalanobis Distance -- Bray Curtis Distance -- Canberra Distance -- Binary or Boolean Distance Metrics -- L0 Norm -- Hamming Distance -- Jaccard Similarity and Dissimilarity -- Descriptor Representation -- Coordinate Spaces, Complex Spaces -- Cartesian Coordinates -- Polar and Log Polar Coordinates -- Radial Coordinates -- Spherical Coordinates -- Gauge Coordinates -- Multivariate Spaces, Multimodal Data -- Feature Pyramids -- Descriptor Density -- Interest Point and Descriptor Culling -- Dense vs. Sparse Feature Description -- Descriptor Shape Topologies -- Correlation Templates -- Patches and Shape -- Single Patches, Sub-Patches -- Deformable Patches -- Multi-Patch Sets -- TPLBP, FPLBP -- Strip and Radial Fan Shapes -- D-NETS Strip Patterns -- Object Polygon Shapes -- Morphological Boundary Shapes -- Texture Structure Shapes -- Super-Pixel Similarity Shapes -- Local Binary Descriptor Point-Pair Patterns -- FREAK Retinal Patterns -- Brisk Patterns -- ORB and BRIEF Patterns -- Descriptor Discrimination -- Spectra Discrimination -- Region, Shapes, and Pattern Discrimination. | |
| 505 | 8 | _aGeometric Discrimination Factors -- Feature Visualization to Evaluate Discrimination -- Discrimination via Image Reconstruction from HOG -- Discrimination via Image Reconstruction from Local Binary Patterns -- Discrimination via Image Reconstruction from SIFT Features -- Accuracy, Trackability -- Accuracy Optimizations, Sub-Region Overlap, Gaussian Weighting, and Pooling -- Sub-Pixel Accuracy -- Search Strategies and Optimizations -- Dense Search -- Grid Search -- Multi-Scale Pyramid Search -- Scale Space and Image Pyramids -- Feature Pyramids -- Sparse Predictive Search and Tracking -- Tracking Region-Limited Search -- Segmentation Limited Search -- Depth or Z Limited Search -- Computer Vision, Models, Organization -- Feature Space -- Object Models -- Constraints -- Selection of Detectors and Features -- Manually Designed Feature Detectors -- Statistically Designed Feature Detectors -- Learned Features -- Overview of Training -- Classification of Features and Objects -- Group Distance: Clustering, Training, and Statistical Learning -- Group Distance: Clustering Methods Survey, KNN, RANSAC, K-Means, GMM, SVM, Others -- Classification Frameworks, REIN, MOPED -- Kernel Machines -- Boosting, Weighting -- Selected Examples of Classification -- Feature Learning, Sparse Coding, Convolutional Networks -- Terminology: Codebooks, Visual Vocabulary, Bag of Words, Bag of Features -- Sparse Coding -- Visual Vocabularies -- Learned Detectors via Convolutional Filter Masks -- Convolutional Neural Networks, Neural Networks -- Deep Learning, Pooling, Trainable Feature Hierarchies -- Summary -- Chapter 5: Taxonomy of Feature Description Attributes -- Feature Descriptor Families -- Prior Work on Computer Vision Taxonomies -- Robustness and Accuracy -- General Robustness Taxonomy -- Illumination -- Color Criteria -- Incompleteness -- Resolution and Accuracy. | |
| 505 | 8 | _aGeometric Distortion. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 655 | 4 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _aKrig, Scott _tComputer Vision Metrics _dBerkeley, CA : Apress L. P.,c2014 _z9781430259299 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6422722 _zClick to View |
| 999 |
_c15820 _d15820 |
||