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An Efficient and Fast Active Contour Model for Salient Object Detection. Authors: Farnaz Shariat , Riadh Ksantini, Boubakeur Boufama shariatf@uwindsor.ca ksantini@uwindsor.ca boufama@uwindsor.ca University of Windsor May 2009. Presentation Outline . Introduction Active contours

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an efficient and fast active contour model for salient object detection

An Efficient and Fast Active Contour Model for Salient Object Detection

Authors:

Farnaz Shariat ,

Riadh Ksantini,

Boubakeur Boufama

shariatf@uwindsor.ca

ksantini@uwindsor.ca

boufama@uwindsor.ca

University of Windsor

May 2009

presentation outline
Presentation Outline
  • Introduction
    • Active contours
    • Level sets
  • A variational level set method
  • Polarity information
  • The active contour model using polarity information
  • Experiments

An Efficient and Fast Active Contour Model for Salient Object Detection

active contours
Active Contours
  • Image Segmentation solution
  • Based on Deformable models
  • Find equation
  • Parametric Represent curves and surfaces explicitly in their parametric forms during deformation; (Kass 1987 )
  • Geometric Based on curve evolution and the level set method, Represent curves and surfaces implicitly as a level set of a function; (Caselles 1993)

An Efficient and Fast Active Contour Model for Salient Object Detection

active contours cont d
Active Contours(cont’d)
  • limitations of parametric AC
    • Initial contour dependant
    • Same topology
  • Geometric ACs provide elegant solution
    • Based on level set , curve evolution

An Efficient and Fast Active Contour Model for Salient Object Detection

level sets
Level sets
  • Main Idea:
    • Closed interface Γ, velocity v
    • Goal: motion of the interface
  • Osherand Sethian1988 idea:
    • Represent the interface by implicit smooth function φ
      • φ =(x, t) =0 , Γ
      • φ =(x, t) <0 , Γin
      • φ =(x, t) >0 , Γout

An Efficient and Fast Active Contour Model for Salient Object Detection

level sets cont d
Level sets(cont’d)
  • Remains a valid function change topology
  • Computationally simple
  • Start far from boundaries

An Efficient and Fast Active Contour Model for Salient Object Detection

level sets cont d1
Level sets (cont’d)

Classical vs. Variational

Variational methods are suitable for incorporating additional information

originated from minimizing the energy function

originated from a certain evolution PDE of a parameterized curve

An Efficient and Fast Active Contour Model for Salient Object Detection

level sets cont d2
Level sets (cont’d)

Reshaping (re-initialization)

φ can develop shocks inaccurate computation

To avoid

Initialize φ as a signed distance function

Reshape φ as a signed distance function regularly

Drawbacks

Displacement of the zero level set

Increasing of the number of iteration

Expensive, Complex

Ad hoc manner

An Efficient and Fast Active Contour Model for Salient Object Detection

variational level set
Variational level set
  • C. Li, C. Xu, C. Gu, M.D. Fox, “Level set evolution without re-initialization: a new variational formulation”, CVPR, 2005
  • Energy function :

Keeping the function close to sign distance function

Moving toward the boundaries

An Efficient and Fast Active Contour Model for Salient Object Detection

variational level set cont d
Variational level set (cont‘d)
  • Advantages
    • Initialization is automatic
    • No need for reinitialize
    • Computationally effective

Active contour result using Li’s algorithm

An Efficient and Fast Active Contour Model for Salient Object Detection

variational level set cont d1
Variational level set (cont‘d)
  • Problem
    • Noisy background
    • Textured background
  • Proposed Solution
    • Using “Polarity information” instead of gradient with “Level sets”

Active contour result using Li’s algorithm

An Efficient and Fast Active Contour Model for Salient Object Detection

polarity information
Polarity information
  • Common edge detectors
  • Polarity[Carson, 1997], discriminates boundaries
    • A measure of the extent to which the gradient vectors in a certain neighbourhood all point in the dominant orientation .

#gradient vectors in are in + side of dominant orientation

#gradient vectors in are in - side of dominant orientation

An Efficient and Fast Active Contour Model for Salient Object Detection

polarity values
Polarity Values

Noise

Texture

Edge

E+= E-

i.e. E-=0,

E+!=0

E+ ~ 0

E- ~ 0

An Efficient and Fast Active Contour Model for Salient Object Detection

the active contour model using polarity information
The Active Contour Model Using Polarity Information
  • Instead of Gradient in Eextuse Polarity
    • Combine “Polarity based stopping function” with “Variational Level Set”

An Efficient and Fast Active Contour Model for Salient Object Detection

the active contour model using polarity information1
The Active Contour Model Using Polarity Information
  • The final energy function is

An Efficient and Fast Active Contour Model for Salient Object Detection

the active contour model using polarity information2
The Active Contour Model Using Polarity Information
  • Then by using energy minimization method to minimize the total energy it can reach to:
  • And by using gradient descent, the approximation of the above formula is:

An Efficient and Fast Active Contour Model for Salient Object Detection

results
Results

An Efficient and Fast Active Contour Model for Salient Object Detection

results1
Results

An Efficient and Fast Active Contour Model for Salient Object Detection

results2
Results

An Efficient and Fast Active Contour Model for Salient Object Detection

results3
Results

An Efficient and Fast Active Contour Model for Salient Object Detection

results4
Results

An Efficient and Fast Active Contour Model for Salient Object Detection

any questions

Any Questions

Thank you for your Attention