1. Muazzam Shehzad Quratulain Muazzam
LEVEL SETS
Muazzam Shehzad Quratulain Muazzam

2. HELLO!
I am Muazzam Shehzad I am here because I love to give presentations.
You can find me

4. Contents Image Segmentation Overview. Implicit & Explicit Concepts.
Level Set Method overview.
Drawbacks in old Techniques and Level Set advantages.
How LS method Works?
LS Equations
Guassian Mixture Model
Bhattacharyya based GMM Distance
Related Researches and Implementation
Applications

5. OVERVIEW
Image Sementation-> using PDE( Power Differential Equations).
Most influential PDE based image segmentation methods are based on active contours.
Sub catogaries
Region based segmentation method
Edge based segmentation method
Segmentation methods based on active contours(Level Set) which use global optimization terms for curve evolution, resulting in segmentation results which are more robust to noisy image data.
Explicit vs. implicit contours
why level sets?

6. Explicit vs. implicit
Explicit interface representation one explicitly writes down the points that belong to the interface ∂Ω={−1,1}.
Implicit interface representation defines the inter-face as the iso-contour of some functions

7. Continuous Space and Infinite-dimensional Optimization
Motion of a hyper surface
Evolution Eq.
Energy minimization: E(C) min

8. Evolution of Explicit Boundaries

9. Evolution of Explicit Boundaries

10. Evolution of Explicit Boundaries

11. Statistical Learning of Explicit Shapes

12. Alignment of Explicit Contours

13. Segmentation with Statistical Shape Prior

14. Tracking with Kernel Shape Prior

15. Limitations of Explicit Representations

16. Why level Sets ?Advantages
The explicit representation by default does not allow the evolving contour to undergo topological changes such that the segmentation of several objects or multiply-connected objects is not straight-forward.
The segmentations obtained by a local optimization method are bound to depend on the initialization. The Snake algorithm is known to be quite sensitive to the initialization. For many realistic images, the segmentation algorithm tends to get stuck in undesired local minima—in particular in the presence of noise.
The Snakes approach lacks a meaningful probabilistic interpretation. Extensions to other segmentation criteria—such as color, texture or motion—are not straight-forward.
Active Contour /Snake need preprocessing of image(noise removal,intensity map).
In Snake approach issues with narrow structures: big trouble in brain images.

17. HELLO! I am Quratulain I am here to share some knowledge on Level
Sets(An Efficient Segmentation Method)

18. Level Set Method
There is a surface, it intersects a plane, that gives us a contour
With image segmentation, the surface is updated with forces derived from the image

19. The Level-Set Methods
Existing level sets(LS) methods formulate the energy terms in
Unsupervised manner.
Supervised manner.

20. The level-set equation
Image I /
The level-set equation

21. Regularization term for level sets

22. Regularization

23. Gaussian Mixture Model
Area term is found by this
Do Cumulative minimization of distance between the training and testing distribution using different distance formulas e.g

24. Bhattacharyya based GMM Distance

25. Active Research & Implementation
LEVEL SET SEGMENTATION OF DERMOSCOPY IMAGES

26. Cond…
Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images.

27. Applications Computational physics Fluid mechanics Optimal design
Computer Graphics
Computer Vision.
………

28. References

29. THANKS!
Any questions?