Nick McCurdy
December 02, 2014
220

Active Contour Research

Course research on the active contour technique in computer vision.

Nick McCurdy

December 02, 2014

Transcript

1. Active Contour Research
by Nicolas McCurdy

2. What is active contour?
● A way of finding the curved outline of an
object while avoiding any background noise.
● It works for 2D images, but similar
techniques can be applied to 3D images.
● Also known as snakes.

3. How it works
1. Create the snake (a connected series of
vertices) at some distance from the object.
2. Minimize the energy iteratively, which
gradually moves the points of the snake
closer to the contour of the object.
3. Stop when the energy is minimized (the
snake’s contour matches the object’s
contour).

4. Visual example
Source: http://commons.wikimedia.org/wiki/File:Snake-contour-example.jpg

5. Active contour with edges
Edge detection algorithms are used on each
iteration to determine where the object is (and
to adjust the current contour appropriately).
● Existing edge detection algorithms can be
used.

6. Active contour with edges
● Only objects with gradient edges can be
detected.
● Usually, the points on the contour can only
move in, not out.

7. Summary of “Active Contours Without Edges”
This model is not based on a stopping edge
detection function. Instead, the image is
segmented by minimizing an energy.
● Works for images with and without gradients.
● Supports finding smooth edges and even
disconnected edges.

8. Part 1: Create a model with a fitting term
To find the curve, minimize the fitting term by
moving the curve C closer to the boundary of
the image in multiple iterations.
Summary of “Active Contours Without Edges”

9. Summary of “Active Contours Without Edges”

10. Part 2: Mumford-Shah functional
For this approach to active contour, the
Mumford-Shah functional is used to help
segment the image into multiple objects before
the fitting term is minimized.
Summary of “Active Contours Without Edges”

11. Part 3: Level set formulation
The level set method can be used to solve the
specific case of the minimal partition problem
that results from segmenting the image with
this technique. The technique described in this
paper uses the level set method to translate the
Mumford-Shah model to segment the image.
Summary of “Active Contours Without Edges”

12. Conclusion
● The active contour’s points are computed
and moved in iterations until the solution is
stationary.
● This is the default method used in MATLAB’
s activecontour function.
Summary of “Active Contours Without Edges”

13. Example
Summary of “Active Contours Without Edges”

14. Using activecontour in MATLAB
● A is a 2D grayscale image.
● mask is a binary image.
● n is the number of iterations.
● method is the method used for active
contour (‘Chan-Vese’ or ‘edge’).

15. Using activecontour in MATLAB
● Defaults to 100 iterations.
● Defaults to the ‘Chan-Vese’ method (as
described previously).

16. Using activecontour in MATLAB
Example
figure, imshow(bw);
title('Segmented Image');

17. Using activecontour in MATLAB
Example Results

18. References
● MATLAB’s activecontour documentation
● Papers
○ [1] T. F. Chan, L. A. Vese, Active contours without
edges. IEEE Transactions on Image Processing,
Volume 10, Issue 2, pp. 266--‐277, 2001
○ [2] V. Caselles, R. Kimmel, G. Sapiro, Geodesic
active contours. International Journal of Computer
Vision, Volume 22, Issue 1, pp. 61--‐79, 1997.