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Image Deformation Using Moving Least Squares

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Image Deformation Using Moving Least Squares. Scott ... Presented by Yun-Feng Chou. Input. Affine. Similarity. Rigid. Outline. Introduction. Previous work ... – PowerPoint PPT presentation

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Title: Image Deformation Using Moving Least Squares


1
Image Deformation Using Moving Least Squares
  • Scott Schaefer, Travis McPhail, Joe Warren
  • SIGGRAPH 2006

Presented by Yun-Feng Chou
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Input
Affine
Similarity
Rigid
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Outline
  • Introduction
  • Previous work
  • MLS with points
  • MLS with line segments
  • Conclusions

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Introduction
  • Image deformation
  • Animation, morphing, medical imaging
  • Controlled by handles points, lines
  • Function f
  • Interpolation f(pi)qi (handles)
  • Smoothness smooth deformations
  • Identity qipi?f(x)x
  • Similar to scattered data interpolation

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Previous Work
  • Grid/Polygon handles
  • Free-form deformations Sederberg and Parry 1986
    Lee et al. 1995
  • Require aligning grid lines with image features
  • Line handles
  • Beier and Neely 1992
  • Undesirable folding
  • Point handles
  • RBF
  • Thin-plate splines Bookstein 1989
  • Local non-uniform scaling and shearing
  • As-rigid-as-possible deformations
  • Igarashi et al. 2005
  • Solve on the whole triangulation
  • Discontinuities
  • 2D shape deformation using nonlinear least
    squares optimization
  • Weng et al. 2006

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As-Rigid-As-Possible Shape Manipulation
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Thin-plate spline
As-rigid-as-possible
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Previous Work
  • Grid/Polygon handles
  • Free-form deformations Sederberg and Parry 1986
    Lee et al. 1995
  • Require aligning grid lines with image features
  • Line handles
  • Beier and Neely 1992
  • Undesirable folding
  • Point handles
  • RBF
  • Thin-plate splines Bookstein 1989
  • Local non-uniform scaling and shearing
  • As-rigid-as-possible deformations
  • Igarashi et al. 2005
  • Solve on the whole triangulation
  • Discontinuities
  • 2D shape deformation using nonlinear least
    squares optimization
  • Weng et al. 2006

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Moving Least Squares Deformation
  • Build image deformations based on handles (pi,qi)
  • Interpolation
  • Smoothness
  • Identity
  • Given a point v, solve for the best lv(x)
  • Deformation function f(v)lv(v)
  • Locally defined --- MOVING Least Squares
  • Satisfy the three properties

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Affine Transform
  • Affine transform lv(x)xMT
  • Translation can be removed Tq-pM
  • So, lv(x)(x-p)Mq
  • The new cost function
  • M could be different class of transformations

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Affine Deformations
  • Solution
  • The deformation function
  • Aj can be precomputed
  • Contains non-uniform scaling and shear

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Similarity Deformations
  • Only include translation, rotation, and uniform
    scaling
  • Remove shear from deformation

22
Similarity Deformations (Cont.)
  • A special subset of affine transforms
  • Translation
  • Rotation
  • Constraints Uniform-scaling
  • Requirement MTM?2I
  • Define , where M2M1-
  • Cost function (Least squares problem) still
    quadratic in M1
  • where (x,y)-(-y,x)

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Similarity Deformations (Cont.)
  • Solution for matrix M
  • where
  • Solution for deformation function
  • where
  • Property
  • Preserves angles better than affine deformations
  • Local scaling can hurt realism

24
Rigid Deformations
  • Deformations should not include scaling and shear
  • Alexa 2000 Igarashi et al. 2005
  • Best rigid transformation can be found from best
    similarity transformation
  • Remove local uniform scaling MTMI

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Rigid Deformations (Cont.)
  • Solution for matrix M
  • Solution for deformation function
  • where , and Ai is as in similarity
    deformations.
  • Limited precomputation

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Some Rigid Deformation Results
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Deformation with Curves
  • Handles are control curves instead of control
    points
  • Cost function
  • T still can be removed

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Affine Lines
  • Represent line segments as matrix
    products
  • Cost function
  • Minimizer

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Affine Lines (Cont.)
  • Deformation

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Similarity Lines
  • Cost function
  • Minimizer

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Similarity Lines (Cont.)
  • Deformation

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Rigid Lines
  • Derive from similarity lines
  • Deformation

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Implementation
  • Pixel by pixel --- expensive
  • Deformation on a downsampled grid
  • Fill quads using bilinear interpolation

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Timing
Grid size 100x100
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Limitation
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Conclusions
  • Moving Least Squares
  • 2x2 linear system at each grid point
  • Real-time
  • Similarity and rigid transformations
  • More realistic results
  • Extension line segments
  • Closed-form expressions

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Conclusions (Cont.)
  • Future Work
  • Adding topological information
  • Generalizing to 3D to deform surfaces
  • Handles can be any curves
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