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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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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 • Computer Vision Image Interpretation • Face Recognition • Medical image analysis
Introduction-Application • Face Recognition • Figure: Example face image annotated with landmarks
Introduction-Application • Medical image analysis • Figure: Example MR image of knee with carilage outlined
Introduction-History • History • Snake (Active Contour Models) --1989 • ASM (Active Shape Models) --1995 • AAM (Active Appearance Models) --1998
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 • 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 • 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
ASM • Variable parameters position scale orientation shape parameters
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
ASM • The linear shape model of an independent AAM
ASM • Build the model • Get shapes from a set of annotated images of typical examples • Normalize • PCA
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
ASM • Figure: Search using Active Shape Model of a face
AAM-Active Appearance Models • Shape • Appearance • Model Instantiation • Fitting
AAM • Appearance • Warp each example image • Sample • Normalize • PCA
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
AAM • Figure: a ‘shape-free’ image patch
AAM • Normalize • To minimize the effect of global lighting variation • PCA • The appearance expression the appearance parameters
AAM • Figure: The linear appearance variation of an independent AAM
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
AAM • Figure: An example of AAM instantiation
AAM • Fitting • Naturally, we want to minimize the error between and • Denote as:
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 • Alignment algorithms • Automatic landmark • View-Based appearance models • Applications • …
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 • …