CVonline: Vision Geometry and Mathematics


  1. Basic Representations
    1. Coordinate Systems
      1. Cartesian: Affine, Rectangular
      2. Cylindrical
      3. Hexagonal
      4. Log-Polar
      5. Mean Value Coordinates
      6. Polar
      7. Spherical
    2. Digital Topology
    3. Dual Space
    4. Homogeneous Coordinates
    5. Pose/Rotation/Orientation Representations
      1. Axis-angle
      2. Clifford Algebra
      3. Euler Angles
      4. Exponential Map
      5. Quaternion/Dual-Quaternion
      6. Rotation Matrix (See also Homogeneous Coordinates)
      7. Rotation/Slant/Tilt
      8. Yaw/Pitch/Roll
  2. Distance Metrics
    1. Affine
    2. Algebraic
    3. Bhattacharyya
    4. Chi-squared
    5. Curse of dimensionality
    6. Earth Mover's/Optimal Mass Transport/Monge-Kantorovich
    7. Euclidean
    8. Fuzzy Intersection
    9. Hausdorff
    10. Jeffrey-Divergence
    11. Kullback-Leibler Divergence (KL)
    12. Mahalanobis
    13. Manhatten or city-block
    14. Minkowski-form
    15. Procrustes
    16. Procrustes Average
    17. Quadric Form
    18. Specific Structure Similarity
      1. Curve Similarity
      2. Region Similarity
      3. Volume Similarity
  3. Elementary Mathematics for Vision
    1. Coordinate Systems, Vectors, Matrices, Derivatives and Gradients, Probability
    2. Derivatives in sampled images
  4. Function Optimization
    1. 1D Function Optimization and Golden Section
    2. Constrained Optimization and Lagrange Multipliers
    3. Multi-Dimensional Optimization
      1. Derivative Free Search
      2. Global Optimization
        1. Ant Colony Optimization
        2. Downhill Simplex
        3. Genetic Algorithms (See Genetic Algorithms/Programming)
        4. Graduated Non-Convexity and Multi-Resolution Methods
        5. Markov Random Field Optimization
        6. Particle Swarm Optimization
        7. Simulated Annealing
      3. Optimization With Derivatives
        1. Levenberg-Marquardt
        2. Newton and Gradient Descent Algorithms
    4. Optimization Model Selection
    5. Variational Methods
  5. Linear Algebra for Computer Vision
    1. Eigenfunctions
    2. Eigenvalues/Eigenvectors
    3. Principal Component and Related Approaches
      1. Dimensionality Reduction
      2. Discriminant Analysis
      3. Factor Analysis
      4. Fisher Linear Discriminant Transformation
      5. Independent Component Analysis
      6. Kernel Linear Discriminant Analysis
      7. Kernel Principal Component Analysis
      8. Locality Preserving Projections
      9. Non-Negative Matrix Factorization
      10. Optimal Dimension Estimation
      11. Principal Component Analysis/Karhunen-Loeve transformation
      12. Principal Geodesic Analysis (PGA)
      13. Probabilistic Principal Component Analysis
      14. Rao-Blackwell Dimensionality Reduction
    4. Sammon Mapping
    5. Singular Value Decomposition (SVD)
    6. Structure Tensor
  6. Multi-Sensor/Multi-View Geometries
    1. 3D Reconstruction
      1. 2D Projections
      2. Reconstruction from Multiple Images/Orthogonal Views
      3. Slice-based Reconstruction (e.g. PET/CAT/MRI)
    2. Affine and Projective Stereo
    3. Baseline
      1. Narrow Baseline Stereo
      2. Wide Baseline Stereo
    4. Binocular Stereo Algorithms
      1. Cooperative Algorithms
      2. Disparity
        1. Subpixel Disparity
      3. Dense Stereo Matching Approaches
      4. Dynamic Programming
      5. Feature Matching Stereo Algorithms
      6. Gradient Matching Stereo Algorithms
      7. Image Rectification
        1. Planar Rectification
        2. Polar Rectification
      8. Log-Polar Stereo
      9. Multi-Scale Stereo Algorithms
      10. Panoramic Image Stereo Algorithms
      11. Phase Matching Stereo Algorithms
      12. Region Matching Stereo Algorithms
      13. Weakly/Uncalibrated Approaches
      14. Spherical Stereo
    5. Epipolar/Multi-View Geometry
      1. Absolute Conic
      2. Absolute Quadric
      3. Epipolar Geometry Definitions
      4. Essential Matrix
      5. Fundamental Matrix
      6. Grassmannian Space/Plucker Embedding
      7. Homography Tensor
      8. Transfer and Novel View Synthesis
      9. Trifocal/Quadrifocal Tensor
    6. Image Based Modelling/Plenoptic Modelling
    7. Image Feature Correspondence Constraints
      1. Active Stereo
      2. Disparity Gradient Limit
      3. Disparity Limit
      4. Epipolar Constraint
      5. Feature Contrast
      6. Feature Orientation
      7. Grey-level Similarity
      8. Lipschitz Continuity
      9. Ordering
      10. Surface Continuity
      11. Surface Smoothness
      12. Uniqueness
      13. Viewpoint Constraint
      14. View Consistency Constraint
    8. Multi-View Matching
    9. Scene Reconstruction/Surface Interpolation
      1. Adaptive Meshing
      2. Constrained Reconstruction
      3. Membrane/Thin Plate Models
      4. Texture Mapping
      5. Triangulation
      6. Volumetric Reconstruction
    10. Trinocular (and more) Stereo
  7. Parameter Estimation
    1. Bayesian Methods
    2. Constrained Least Squares
    3. Linear Least Squares
    4. Optimization (See Functional Optimization)
    5. Robust Techniques (See Robust Estimators)
  8. Probability and Statistics for Computer Vision
    1. Autoregression
    2. Basic Statistics and Bayes Rule in Vision
    3. Bayesian Inference Networks (See generic entry)
    4. Causal Models
    5. Correlation
    6. Covariance and Mahalanobis Distance in Vision
    7. Dempster-Shafer and Evidence Theory
    8. Distribution Mode Analysis
    9. Gaussian / Normal Distribution
    10. Heteroscedastic Noise and HEIV Regression
    11. Homoscedastic Noise
    12. Hidden Markov Models
    13. Honest Probabilities
    14. Hypothesis Testing
    15. Information Theory
    16. Kalman Filters
      1. Unscented Kalman Filter
    17. Kernel Canonical Correlation
    18. Kernel Regression
    19. Least Mean Square Estimation and Estimators
    20. Least Median Square Estimation and Estimators
    21. Log-Normal Distribution
    22. Logistic Regression
    23. Maximum Likelihood
    24. Model Fitting
    25. Monte Carlo Methods for Vision
    26. Marked Point Process
    27. Markov Chain Methods for Vision
    28. Markov Random Fields
      1. Applications
      2. Conditional Random Fields
      3. Multi-level MRF
      4. Optimization Methods
        1. Approximate Variational Extremum
        2. Gibbs Sampling
        3. Graduated Nonconvexity
        4. Graph Cuts
        5. Iterated Conditional Modes
        6. "Modern" Graph Cut
        7. Simulated Annealing
      5. Theory
    29. Mixture Models and Expectation Maximization (EM)
      1. Poisson Mixture Model
    30. Normalization
    31. Non-Parametric Methods
    32. Poisson Distribution
    33. Probability Density Estimation
    34. Random Number Generation for Vision
    35. Robust Estimators
    36. Useful Distributions
  9. Projection Geometries and Transformations
    1. Affine
    2. Anamorphic/Catadioptric
    3. Central Projection
    4. Euclidean
    5. Homography
    6. Hierarchy of Geometries
    7. Orthographic
    8. Paraperspective
    9. Perspective
    10. Plane Projection
    11. Projective Space (3D)
    12. Real Camera Projection
    13. Similarity
    14. Weak-Perspective
  10. Properties and Invariants of Projection
    1. Absolute Points
    2. Affine Invariants
    3. Collineations
    4. Conics
    5. Coplanarity Invariants
    6. Cross Ratio
    7. Differential Invariants
    8. Duality
    9. General Projective Invariants
    10. Integral Invariants
    11. Laguerre Formula
    12. Pencil of Lines
    13. Quasi-Invariants
    14. Structural Invariants
  11. Relational Shape Descriptions
    1. Curves
      1. Adjacency/Connectedness
      2. Relative Curvature
      3. Relative Length
      4. Relative Orientation
      5. Separation
    2. Regions
      1. Adjacency/Connectedness
      2. Relative Area/Size
      3. Separation
    3. Surfaces
      1. Adjacency/Connectedness
      2. Relative Area/Size
      3. Relative Orientation
      4. Separation
    4. Volumes
      1. Adjacency/Connectedness
      2. Relative Orientation
      3. Relative Volume/Size
      4. Separation
  12. Shape Properties (See also Geometric Representation of Model Features)
    1. Geometric Morphometrics
    2. Kendall's Shape Space
    3. Points and Local Invariants
    4. Curves and Curve Invariants (See also Curves)
      1. Affine Arc Length and Affine Curvature
      2. Arc Length
      3. Bending Energy
      4. Chord Distribution
      5. Curvature, Torsion, Curvature Radius
      6. Differential Geometry, Frenet Frame, Frenet-Serret Formulas
      7. Invariant Points: Inflections/Bitangents
    5. Image Regions and Region Invariants
      1. Angularity ratio
      2. Area, Perimeter
      3. Boundary Properties
      4. Center-of-Mass
      5. Convexity Ratio
      6. Eccentricity, Circularity, Compactness, Elongatedness
      7. Elongation Factor
      8. Euler number/Genus
      9. Extremal Points
      10. Feret's Diameter, Martin's Diameter
      11. Fourier Descriptors
      12. Minimum Bounding Rectangle
      13. 2D Moments and their Invariants
        1. Affine Moments
        2. Bessel-Fourier Moments
        3. Binary Moments
        4. Color Moments
        5. Eigenmoments
        6. Fourier-Mellin Moment Invariants
        7. Gaussian-Hermite Moments
        8. Grey-Level or Texture Moments
        9. Hahn Moments
        10. Krawtchouk Moments
        11. Legendre Moments
        12. Orthogonal Moments: Legendre, Zernike
        13. Racah Moments
        14. Tchebichef/Chebichev Moments
        15. Velocity Moments
        16. Zernike Moments
      14. Orientation
      15. Sphericity ratio
      16. Rectangularity
      17. Rectilinearity
      18. Roundness ratio
      19. Topological Descriptors
      20. Wadell's circularity shape ratio
    6. Surfaces
      1. Apparent Contour and Local Geometry
      2. Common Shape Classes and Representations
        1. Cone
        2. Cyclide
        3. Cylinder
        4. Ellipsoid/Sphere
        5. Membrane/Thin Plate Spline (See here)
        6. Plane
        7. Polyhedra
        8. Quadric
        9. Torus
      3. Fundamental Forms
      4. Gauge Coordinates
      5. Hessian
      6. Laplace-Beltrami Operator
      7. Metric Determinant
      8. Principal Curvatures and Directions and other Local Shape Representations
        1. Deviation from Flatness
        2. Gauss-Bonnet Surface Description
        3. Gaussian Curvature
        4. Koenderink's Shape Classification
        5. Mean Curvature
        6. Mean and Gaussian Curvature Shape Classification
        7. Minimal Points
        8. Parabolic Points
        9. Ridges and Valleys
        10. Umbilics
      9. Quadratic Variation
      10. Ricci Flow
      11. Surface Area
      12. Surface Normals and Tangent Planes/Tangent Spaces
      13. Surface Orientation and Gradient Space
    7. Symmetry (See also Symmetry Detection)
      1. Affine
      2. Bilateral
      3. Rotation
      4. Skew
    8. Volumes
      1. Elongatedness
      2. 3D Moments and Moment Invariants
      3. Volume
  13. Transformations (Geometric), Registration and Pose Estimation Methods
    1. 2D to 2D Pose Estimation Methods
      1. Line-Based Methods
      2. Point-Based Methods
    2. 2D to 3D Pose Estimation Methods
      1. Line-Based Methods
      2. Point-Based Methods - PnP - Perspective N Point Problem
    3. 3D to 3D Pose Estimation Methods
      1. Line-Based Methods
      2. Point-Based Methods
    4. Affine Transformation
      1. Minimal Data Estimation
      2. Least-square Estimates
      3. Robust Estimates
    5. Bundle Adjustment
    6. Euclidean Transformation
      1. Least-square Euclidean Transformation Estimates
      2. Minimal Data Euclidean Transformation Estimation
      3. Robust Euclidean Transformation Estimates
    7. Homography Transformation
      1. Least-square Homography Transformation Estimates
      2. Minimal Data Estimation
      3. Robust Homography Transformation Estimates
    8. Kalman Filter Pose Estimation Methods
    9. Partially Constrained Pose
      1. Incomplete Information
      2. Intrinsic Degrees of Freedom
    10. Projective Transformation
      1. Least-square Estimates
      2. Minimal Data Estimation
      3. Robust Estimates
    11. Similarity Transformation
      1. Least-square Estimates
      2. Minimal Data Estimation
      3. Robust Estimates

Return to CVentry top level


Valid XHTML 1.0 Strict

© 2007 robert fisher