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A Brief Introduction to Active Appearance Models. Topics of the talk. Introduction AAM Future and Related works Reference. Introduction. What is AAM? Non-linear, generative, parametric models What can AAM do? Statistical models Depend on the problem

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Presentation Transcript
topics of the talk
Topics of the talk
  • Introduction
  • AAM
  • Future and Related works
  • Reference
introduction
Introduction
  • What is AAM?
    • Non-linear, generative, parametric models
  • What can AAM do?
    • Statistical models
    • Depend on the problem
  • Computer Vision Image Interpretation
    • Face Recognition
    • Medical image analysis
introduction application
Introduction-Application
  • Face Recognition
    • Figure: Example face image annotated with landmarks
introduction application1
Introduction-Application
  • Medical image analysis
    • Figure: Example MR image of knee with carilage outlined
introduction history
Introduction-History
  • History
    • Snake (Active Contour Models) --1989
    • ASM (Active Shape Models) --1995
    • AAM (Active Appearance Models) --1998
snake active contour models
Snake- Active Contour Models
  • Start with a curve near the object
    • Discrete snake: spline with n control points
  • Evolve the curve to fit the boundary
    • Minimize the energy function
    • Original formulation
snake
Snake
  • Weakness
    • weak constraints
    • high compute cost
    • can not search inside boundary
    • not optimal for known shape because of no prior knowledge
asm active shape models
ASM-Active Shape Models
  • Use prior knowledge from the training set
  • Variable parameters
  • Statistical Shape Models
    • Allow formal statistical techniques to be applied to sets of shapes, making possible analysis of shape differences and changes
slide10
ASM
  • Variable parameters

position

scale

orientation

shape parameters

slide11
ASM
  • Shape
    • define the shapes as the coordinates of the v vertices that make up the mesh:
    • AAM allow linear shape variation

the shape parameters

slide12
ASM
  • The linear shape model of an independent AAM
slide13
ASM
  • Build the model
    • Get shapes from a set of annotated images of typical examples
    • Normalize
    • PCA
slide14
ASM
  • Use the model for locating
    • Given a rough starting approximation instance
    • Examine a region around, find the best nearby match for each point
    • Update the parameters to best fit the new point
    • Repeat until convergence
slide15
ASM
  • Figure: Search using Active Shape Model of a face
aam active appearance models
AAM-Active Appearance Models
  • Shape
  • Appearance
  • Model Instantiation
  • Fitting
slide17
AAM
  • Appearance
    • Warp each example image
    • Sample
    • Normalize
    • PCA
slide18
AAM
  • Warp each example image
    • Its control points match the mean shape
    • Using
      • Piecewise affine warping

(Delaunay Triangulation algorithm)

      • Thin plate splines
  • Sample
    • The intensity information from shape-normalized image to form a texture vector
slide19
AAM
  • Figure: a ‘shape-free’ image patch
slide20
AAM
  • Normalize
    • To minimize the effect of global lighting variation
  • PCA
  • The appearance expression

the appearance parameters

slide21
AAM
  • Figure: The linear appearance variation of an independent AAM
slide22
AAM
  • Model Instantiation
    • The two equations describe the shape and the appearance variation
    • Given the shape parameters
    • Given the appearance parameters
    • Create warping appearance A from the base mesh S0 to the model shape S
slide23
AAM
  • Figure: An example of AAM instantiation
slide24
AAM
  • Fitting
    • Naturally, we want to minimize the error between and
    • Denote as:
slide25
AAM
  • Fitting Algorithms
    • Inefficient Gradient Descent Algorithms
    • Efficient Ad-Hoc Fitting Algorithms
    • Efficient Gradient Descent Image Alignment
      • Lucas-Kanade Image Alignment
      • Forwards Compositional Image Alignment
      • Inverse Compositional Image Alignment
    • ...
future and related works
Future and Related works
  • Alignment algorithms
  • Automatic landmark
  • View-Based appearance models
  • Applications
reference
Reference
  • T.F. Cootes and C.J. Taylor
    • Statistical Models of Appearance for computer vision
    • Active Appearance Models
    • Active Shape Models-Their Training and Application
  • Iain Matthews and Simon Baker
    • Active Appearance Models Revisited