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Level Set Formulation for Curve Evolution

Computer Science Department. Technion-Israel Institute of Technology. Level Set Formulation for Curve Evolution. Ron Kimmel www.cs.technion.ac.il/~ron. Geometric Image Processing Lab. Implicit representation. Consider a closed planar curve

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Level Set Formulation for Curve Evolution

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  1. Computer Science Department • Technion-Israel Institute of Technology Level Set Formulationfor Curve Evolution Ron Kimmel www.cs.technion.ac.il/~ron • Geometric Image Processing Lab

  2. Implicit representation Consider a closed planar curve The geometric trace of the curve can be alternatively represented implicitly as

  3. Properties of level sets The level set normal Proof.Along the level sets we have zero change, that is , but by the chain rule So,

  4. Properties of level sets The level set curvature Proof.zero changealong the level sets, , also So,

  5. Optical flow Problem: find the velocity also known as `optical flow’ It’s an `inverse’ problem, Given I(t) find

  6. Aperture Problem

  7. Aperture Problem • `Normal’ vertical flow • Horizontal flow can not be computed differentially.

  8. Normal flow Due to the `aperture problem’ only the `normal’ velocity can be locally computed for the normal flow we have Image analysis

  9. Level Set Formulation y C(t) implicit representation of C Then, Proof. By the chain rule Then, Recall that , and x y C(t) level set x Image synthesis

  10. Level Set Formulation • Handles changes in topology • Numeric grid points never collide or drift apart. • Natural philosophy for dealing with gray level images.

  11. Numerical Considerations • Finite difference approximation. • Order of approximation, truncation error, stencil. • (Differential) conservation laws. • Entropy condition and vanishing viscosity. • Consistent, monotone, upwind scheme. • CFL condition (stability examples)

  12. Numerical Considerations Central derivative Forward derivative Backward derivative

  13. Truncation Error Taylor expansion about x=ih Stencils

  14. Numerical Approximations

  15. Conservation Law Rate of change of the amount in a fixed domain G = Flux across the boundaries of G Differential conservation law

  16. Generalized Solution 1D In 1D Weak solution satisfies

  17. Hamilton-Jacobi In 1D: HJ=Hyperbolic conservation laws In 2D: just the `flavor’… Vanishing viscosity, of The `entropy condition’ selected the `weak solution’ that is the `vanishing viscosity solution’ also known as `entropy solution’.

  18. Numerical Schemes Conservation form Numerical flux The scheme is monotone, if F is non-decreasing. Theorem: A monotone, consistent scheme, in conservation form converges to the entropy solution. Yet, up to 1st order accurate ;-( …

  19. Upwind Monotone Upwind scheme For we have upwind-monotone schemes we define Then, and the final scheme is

  20. domain of influence domain of dependence CFL Stability Condition At the limit For 3-point scheme of we need for the numerical domain of dependence to include the PDE domain of dependence

  21. CFL Stability Condition At the limit For 3-point scheme of we need for the numerical domain of dependence to include the PDE domain of dependence

  22. 1D Example Solution Characteristics dx/dt=1 CFL condition Numeric scheme

  23. Numerical viscosity 1D Example where Characteristics Numeric scheme CFL condition

  24. 2D Example Numeric scheme CFL condition

  25. 2D Examples require upwind/monotone schemes Some flows

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