1. Outline Perceptual organization, grouping, and segmentation
Introduction
Region growing
Split-and-merge
File: week13-f.ppt

2. Visual Perception Modeling
Introduction
Segmentation
Roughly speaking, segmentation is to partition the images into meaningful parts that are relatively homogenous in certain sense
11/13/2018
Visual Perception Modeling

3. Visual Perception Modeling
Introduction – cont.
Why is segmentation important
Classification algorithms in general assume that the features are extracted only from the objects/regions that we are interested in
11/13/2018
Visual Perception Modeling

4. Visual Perception Modeling
Introduction – cont.
Why is segmentation difficult
The first difficulty is a representation issue
There are many different kinds of objects, textures
Is there a representation that will apply to all the images
11/13/2018
Visual Perception Modeling

5. Introduction - continued
How can we characterize all these images perceptually?
11/13/2018
Visual Perception Modeling

6. Visual Perception Modeling
Introduction – cont.
Why is segmentation difficult
The first difficulty is a representation issue
There are many different kinds of objects, textures
Is there a representation that will apply to all the images
The second difficulty is to identify first what types of objects are present in the given input image
The third difficulty is to localize the boundaries between regions
11/13/2018
Visual Perception Modeling

7. Visual Perception Modeling
Introduction – cont.
But we can do the task effortless
How have we done so?
11/13/2018
Visual Perception Modeling

8. Visual Perception Modeling
Introduction – cont.
Perceptual organization
Try to understand the principles behind perception by observing and building models for perceptual phenomena
11/13/2018
Visual Perception Modeling

9. Visual Perception Modeling
Introduction – cont.
11/13/2018
Visual Perception Modeling

10. Visual Perception Modeling
Introduction – cont.
Gestalt grouping principles
Proximity
Objects that are close to each other tend to be grouped together
Similarity
Objects that are more similar to one another tend to be grouped together
Closure
Objects that form closed units tend to be grouped together
11/13/2018
Visual Perception Modeling

11. Visual Perception Modeling
Introduction – cont.
Gestalt grouping principles – continued
Good continuation
Common fate
Figure and ground
Subjective contour
11/13/2018
Visual Perception Modeling

12. Visual Perception Modeling
Introduction – cont.
Problems with Gestalt principles
They are NOT computational models
In addition, those factors interfere each other in a given image
11/13/2018
Visual Perception Modeling

13. Visual Perception Modeling
Introduction – cont.
Computational models/implementations
There are generally two kinds of computational models/implementations for segmentation
Based on homogeneity measure to group pixels with similar attributes together
Region growing/split-and-merge
Based on discontinuity of attributes to detect boundaries/contours of regions
Active contours
11/13/2018
Visual Perception Modeling

14. Visual Perception Modeling
Region Growing
Region growing
Is a set of algorithms to group pixels with similar attributes together
The basic idea is to grow from a seed pixel
At a labeled pixel, check its neighbors
If the attributes of its neighbor is similar to the attributes of the labeled pixel, label the neighbor
Repeat until there is no pixel that can be labeled
11/13/2018
Visual Perception Modeling

15. Visual Perception Modeling
Region Growing – cont.
A simple case
The attribute of a pixel is its pixel value
The similarity is given by the difference between the two pixel values
If the difference is smaller than a threshold, we say they are similar
Otherwise they are not
11/13/2018
Visual Perception Modeling

16. Visual Perception Modeling
Region Growing – cont.
Recursive implementation
Given a seed point, call the following recursive function
void RegionGrowing(IMAGE animage, LABEL labelmap, int x, int y, int label)
if (labelmap[x][y] != 0) return;
labelmap[x][y] = label;
for each neighbor nx, ny of pixel x,y
if labelmap[nx][ny]==0
if diff(animage[x][y]-animage[nx][ny]) < threhold,
RegionGrowing(animage, labelmap, nx, ny, label)
end if
end for
return
11/13/2018
Visual Perception Modeling

17. Visual Perception Modeling
Region Growing – cont.
Efficient implementation
Based on scan-line algorithm in graphics
Each time we label a line instead of a pixel
This procedure is much more efficient than the recursive version
11/13/2018
Visual Perception Modeling

18. Visual Perception Modeling
Split and Merge
There is also a different implementation to partition an input image into homogenous regions
Start with the entire image as one region
Then split a region into sub-regions if the variance is larger than a threshold and merge neighboring regions if they are similar
11/13/2018
Visual Perception Modeling

19. Visual Perception Modeling
Split and Merge – cont.
1. Start with the entire image as a single region
2. Pick a region R. If H(R) is false, then split the region into four sub-regions
3. Consider any two or more neighboring sub-regions, R1,R2,...,Rn, in the image. If H(R1 U R2 U ... U Rn ) is true, merge the n regions into a single region.
4. Repeat these steps until no further splits or merges take place
11/13/2018
Visual Perception Modeling

20. Visual Perception Modeling
Split and Merge – cont.
11/13/2018
Visual Perception Modeling

21. Visual Perception Modeling
Split and Merge – cont.
11/13/2018
Visual Perception Modeling

22. Visual Perception Modeling
Split and Merge – cont.
11/13/2018
Visual Perception Modeling