2. Course : T0283 - Computer Vision
Year : 2013
Segmentation Session 7

3. Learning Outcomes
After carefully listening this lecture, students will be able to do the following:
Explain various segmentation techniques and demonstrate their implementation (histogram based segmentation, k-means) using MATLAB and OpenCV (LO2, LO3)
T Computer Vision

4. Lecture Outline Motivation: Recognition Using Region Properties
Region Segmentation
Similarity Constraints
Simple Segmentation
Image Histogram
Segmentation Using Histogram (Simple Case)
Segmentation Using Histogram (Real image histograms)
Recursive Connected Component Algorithm
Sequential Connected Component Algorithm
Phagocyte Algorithm
Likelihood Ratio Test
Difference Between Segmentation and Edge Detection
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5. Segmentation is the process of distinguishing objects in the dataset from their surroundings so as to facilitate the creation of geometric models.
For example, in medical imaging it is often important to measure the shape, surface area, or volume of tissues in the body. Once the dataset is segmented, those quantities are easily measured.
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6. Motivation: Recognition Using Region Properties
Training
For all training samples of each model object
Segment the image
Compute region properties (features)
Recognition
Given an image of unknown object,
Compute its feature vector
Compare with the training set
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7. Region Segmentation
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8. Segmentation Find set of regions R1, R2, ….,Rn such that:
All pixels in region i satisfy some similarity constraint:
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9. Similarity Constraints
All pixels in any sub-image musts have the same gray levels.
All pixels in any sub-image must not differ more than some threshold
All pixels in any sub-image may not differ more than some threshold from the mean of the gray of the region
The standard deviation of gray levels in any sub-image must be small.
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10. Simple Segmentation
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11. Image Histogram
Histogram graphs the number of pixels with a particular gray level as a function of the image of gray levels.
number of pixels
gray
level
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12. Segmentation Using Histogram Simple Case
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13. Segmentation Using Histogram Simple Case
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14. Realistic Histograms Smooth out noise
Convolve hist. by averaging or 1D Gaussian filter
peak
peak
valley
peak
valley
valley
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15. Segmentation Using Histogram Real image histograms
Compute the histogram of a given image.
Smooth the histogram by averaging peaks and valleys in the histogram.
Detect good peaks by applying thresholds at the valleys.
Segment the image into several binary images using thresholds at the valleys.
Apply connected component algorithm to each binary image find connected regions.
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16. Segmentation Using Histograms
Select the valleys as thresholds
Apply threshold to histogram
Label the pixels within the range of a threshold with same label, i.e., a, b, c … or 1, 2, 3 …
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17. Connected Components
Disjoint segments with same labels need to be split
may be added to segment c
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18. Recursive Connected Component Algorithm
Scan the binary image left to right, top to bottom.
If there is an unlabeled pixel with a value of “1”, assign a new label to it.
Recursively check the neighbors of the pixel in step 2 and assign the same label if they are unlabeled with value “1”.
Stop when all the pixels of value “1” have been labeled.
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19. Sequential Connected Component Algorithm
Scan the binary image left to right, top to bottom.
If an unlabeled pixel with a value of “1”, assign a new label to it according to the following rules :
Determine equivalence classes of labels
In the second pass, assign the same label to all elements in an equivalence class.
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20. Sequential Connected Component Algorithm
Equivalence
class
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21. Example: Detecting Finger Tips (marked white)
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22. Recaps: Steps in Seed Segmentation
Compute the histogram.
Smooth the histogram
Detect good peaks
Segment image into binary images using thresholds at the valleys.
Apply connected component algorithm.
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23. Improving Seed Segmentation
Merge small neighboring regions
Region growing step
Split large regions
Region splitting
Remove weak boundaries between adjacent regions
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24. Region Growing Seed segmentation (histogram based seg.)
Region splitting and merging
Phagocyte algorithm
Likelihood ratio test
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25. Region Split and Merge
Splitting: split non uniform region into 4 adjacent regions
Merging: merge similar regions
Stop when no splitting merging are possible
Uniformity/similarity function: P
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26. Example R R R R R R R R R R R T0283 - Computer Vision R R R R R R R R

27. Phagocyte Algorithm Boundary melting
Remove weak boundaries
Similar to region merging
Boundary weakness is based on color similarity R1 R2
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28. Phagocyte Algorithm R1 R2
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29. Phagocyte Merging Heuristics
Merge two regions if
where P1 and P2 are the perimeters of regions R1 and R2.
Merge regions if
Phagocyte
Weakness
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30. Likelihood Ratio Test Two region hypothesis
There is only one region
There are indeed two regions
Intensity at every pixel is independent
Joint probability
Gaussian distribution
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31. Likelihood Ratio Test observing intensity under Gaussian model
Likelihood of region A
x1, x2, …,xm1A
Likelihood of region B
xm1+1, xm1+2, …,xm1+m2B
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32. Likelihood Ratio Test There is 1 region hypothesis
There are 2 regions hypothesis
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33. Likelihood Ratio Test Merge regions if LH<T.
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34. Region Growing Methods
Different algorithms have different number of thresholds
How much merging is required
Stopping criterion
Though simple, seed segmentation succeed by region growing is usually successful
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35. Difference Between Segmentation and Edge Detection
Closed boundary
Edges are usually open
Segmentation provides closed boundaries
Local or global
Edges are computed in the locality
Segmentation is global
Increasing feature vector dimensionality
Does not drastically improve edge detection
Improves segmentation (motion, texture information etc.)
Boundary position
Localized in edge detection
Usually not localized (recent advancements use locality as well)
Especially contour based segmentation
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36. References
Textbook
Forsyth, D., Ponce, J. (2012). Computer Vision: A Modern Approach. 2nd ed. Prentice Hall. ISBN:
Web
Relationship to edge detection, segmentation, and classification
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37. THANK YOU
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