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Computer Vision Metrics : Survey, Taxonomy, and Analysis.

Bibliographic Details
Main Author: Krig, Scott.
Format: eBook
Language:English
Published: Berkeley, CA : Apress L. P., 2014.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • 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.
  • Illuminants, 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.
  • 1950s 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.
  • Local 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.
  • Geometric 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.
  • Geometric Distortion.

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