

-------------------DEMO VIDEOS-------------------


 Paper title: CRF-driven Implicit Deformable Model
 Paper ID #:  635

-------------------------------------------------

 Videos that show the model evolution and convergence:

1. bird.mpg

We used both the model interior during the initialization and training samples
for the ROI texture and intensity; the training samples consist of multiple textures
similar to the bird's various textures. This is the reason why we were able to estimate
the exact bird's boundaries.
------------------------------------------------------------------------------------------

2. bird_init_only.mpg

We used only the model interior statistics and no training samples; this is why we 
undersegmented the bird.
------------------------------------------------------------------------------------------

3. zebra.mpg

We used the model interior and training samples for the zebra's texture and intensity.
------------------------------------------------------------------------------------------

4. cheetah.mpg

We used only the model interior statistics; this is why there are some inaccuracies at the
cheetah's tail (white part)
------------------------------------------------------------------------------------------

5. lizard.mpg

We used both the model interior statistics and training samples for the lizard's multiple
texture patterns.
------------------------------------------------------------------------------------------

6. lizard_init_only.mpg

We used only the model interior statistics and that is we why undersegment the lizard.
------------------------------------------------------------------------------------------

7. lizard2.mpg

Using both the model interior statistics and training samples for this lizard's textures we
were able to find the lizard's boundaries (even without shape priors).
------------------------------------------------------------------------------------------

8. fish.mpg

Using both the model interior and training samples we segmented the the fish from the background.
Note that due to the absence of shape priors, the two fish are merged together.
------------------------------------------------------------------------------------------

9. cardiac.mpg

This is an example of segmenting the left an right ventricle in a cardiac MRI.
------------------------------------------------------------------------------------------

10. cardiac_tagged.mpg

Segmentation of the tagged cardiac MRI.
------------------------------------------------------------------------------------------

11. seeds.mpg

Using only the model interior statistics we were able to handle local intensity and texture variations.
------------------------------------------------------------------------------------------

12. glass.mpg

We were able to segment the glass pyramic where there are variations in the texture pattern and the 
illumination. 
------------------------------------------------------------------------------------------

13. bridge.mpg

Segmentation of the bridge pattern
------------------------------------------------------------------------------------------

14. smoke.mpg

For the same image as in bridge.mpg, we segment the smoke
------------------------------------------------------------------------------------------

15. peppers.mpg

We used training samples that include the dark regions between
the peppers. 
------------------------------------------------------------------------------------------

16. peppers_black.mpg

We show the same example as in peppers.mpg, where the training samples did not
include those dark regions. For the initialization of our model we used the pixels
of the image with the highest probability P(l|I): see eq.(15) of our paper. 
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------

Most of the example images we used can be found at the
Berkeley Segmentation Dataset and Benchmark:

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/


We apologize for the low video quality.




