1. 240-373: Chapter 9: Image Segmentation
Image Processing
Montri Karnjanadecha
: Chapter 9: Image Segmentation

2. 240-373: Chapter 9: Image Segmentation

3. 240-373: Chapter 9: Image Segmentation
Splitting an image up into segments (area or regions)
Each segment holds some property distinct from its neighbor
Basic requirement for identification and classification of objects in a scene
Answering questions like: How many object are there? Where is the background? How large is the object?
: Chapter 9: Image Segmentation

4. 240-373: Chapter 9: Image Segmentation
Segmentation can be approached from two points of view
1. by identifying edges (or lines) that run through an image ( edge operation)
2. by identifying regions (or areas) within an image
(region operation)
Note: 1 and 2 are correlate: completion of an edge detection is equivalent to breaking one region into two
: Chapter 9: Image Segmentation

5. 240-373: Chapter 9: Image Segmentation
Region Operations
Required to cover a substantial area of the scene rather than a small group of pixels
: Chapter 9: Image Segmentation

6. 240-373: Chapter 9: Image Segmentation
Region Operations
Technique 1: Crude region detection
USE: To reconsider an image as a set of regions.
OPERATION: The regions are simply identified as continuous pixels of the same gray level. The boundaries of the regions are at the cracks between the pixels rather than at pixel positions.
: Chapter 9: Image Segmentation

7. 240-373: Chapter 9: Image Segmentation
Region Operations
Notes
This technique may give too many regions to be useful
Bunching (quantizing) technique can be used to reduce number of regions
: Chapter 9: Image Segmentation

8. 240-373: Chapter 9: Image Segmentation
Region Merging
Technique 2: Region merging
USE: To reduce the number of regions, to combine fragment regions, to determine which regions are really part of the same area.
: Chapter 9: Image Segmentation

9. 240-373: Chapter 9: Image Segmentation
Region Merging
OPERATION:
Let s be a crack difference, i.e. the absolute difference in gray levels between two adjacent (above, below, left, or right) pixels
Then given a threshold value, T, we can identify for each crack
i.e. w=1 if the crack is below the threshold (the regions are likely to be the same), or 0 if it is above the threshold.
: Chapter 9: Image Segmentation

10. 240-373: Chapter 9: Image Segmentation
Region Merging
Measure the full length of the boundary of each of the regions that meets at the crack. These will be b1 and b2.
Sum the w’s that are along the length of the crack between the region and calculate
If this is greater than a further threshold, deduce that the two regions should be joined
: Chapter 9: Image Segmentation

11. Region merging example
If we make T=3, then all the cracks are significant
: Chapter 9: Image Segmentation

12. Region merging example
Sum of w’s
Region Boundary With A With B With C A B C
: Chapter 9: Image Segmentation

13. 240-373: Chapter 9: Image Segmentation
Giving sum/min(b1,b2) for A, B, and C as follows:
Region A B C A B C
: Chapter 9: Image Segmentation

14. 240-373: Chapter 9: Image Segmentation
If we take the threshold for combining as 0.95, then B will be combined with C.
If the combining threshold is set lower, then A will also be combined giving the whole image as just one region.
The algorithm combines the small region (4) with the larger region (2), rather than the two larger regions (1-2).
Size of the region can be used instead of the length of the region boundary.
: Chapter 9: Image Segmentation

15. 240-373: Chapter 9: Image Segmentation
Region Splitting
Technique 3: Region splitting
USE: To subdivide part of an image into regions of similar type
OPERATION:
Identify significant peaks in the gray-level histogram
Look in the valleys between the peaks for possible threshold values.
Find splits between the best peaks first
: Chapter 9: Image Segmentation

16. 240-373: Chapter 9: Image Segmentation
Region Splitting
LIMITATIONS:
This technique relies on the overall histogram (Multiple chessboards example)
Example
: Chapter 9: Image Segmentation

17. 240-373: Chapter 9: Image Segmentation
Region Splitting
0 **********************
1 **** the valley is at 1
2 *********
3 *****************
4 ********
: Chapter 9: Image Segmentation

18. 240-373: Chapter 9: Image Segmentation
Region Splitting
Which side should the 1’s go?
Consider both options
(0), (1,2,3,4) (0,1), (2,3,4)
OOOOOO . O OOOOOO . O . .
OOO . OOO OOOOOOO
OO . O OO . O
OO OOO
O . O OOO
The second option less stragglers than the first but is is difficult to see this without trying it.
: Chapter 9: Image Segmentation

19. 240-373: Chapter 9: Image Segmentation
Region Splitting
COMMENTS:
The histogram could just be of a region within the image
For color images, there are 3 histograms to choose from
: Chapter 9: Image Segmentation