Image segmentation - regional segmentation. Regional Growing. Region split & merging Morphological Watersheds - use markers Motion Segmentation.

1. Image
Segmentation.

2. What we have learn so far
Objective of Segmentation.
 Partitioning an image into region.
Early Approaches
 Found boundaries between regions based on discontinuities in intensity levels
Pixel Properties
 Accomplish segmentation via thresholds based on the distribution of pixel
properties.
In this section
 Segmentation techniques to find regions directly. .
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3. 1.
Region Based
Segmentation.

4. Region Growing
Region growing is a procedure that groups pixels
or sub regions into large region based on
predefine criteria for growth.
 Determine the threshold value
 Generate Seed Collection (close to the higher value of the predefine
properties)
 Calculate the pixel differences and compare it with Threshold value.
 If differences < or = threshold value mark it as region. (Concern the 8-
connected)
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5. Example
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Original Image

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Seed Point : 255

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Threshold: 255 - 255

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Threshold : 190 - 255

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Threshold : 155 - 255

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14. Pros
Better in noisy image where
edges are hard to identify
Pros and Cons of Region Growing
Cons
Results depends on the
selected seed point.
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15. Region Splitting and Merging
An alternative method for region image.
 Take full image and check overall pixels are homogeneous or not.
 If not divide image into 4 basic regions.
 Then check each regions are homogeneous or not and if not
divide again that region into 4 sub regions. Continue this process
until met homogeneous regions.
 Then merge if that adjacent separated regions have similar
properties.
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16. Example
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Original Image

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Splitting Image

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Merge and segmented image

20. Segmentation Using Morphological
Watersheds
 Based on visualizing an image in three dimension.
(spatial coordinates vs intensity : topographic
interpretation)
 Produce more stable segmentation results.
 Name refers to behavior of the geological watershed
which separated adjacent drainage basins.
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21. How create topographic surface?
 High intensity denotes peeks and hills.
 Low intensity denotes valleys.
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23. Use of Marker
 This watershed algorithm generally leads to over
segmentation due to noise and other local
irregularities of the gradient.
 Concept of marker is good approach to control over
segmentation.
 Internal marker : object of interest
 External marker : background
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25. The use of motion in segmentation
 Used by humans and many other animals to extract
objects or regions of interest from a background of
irrelevant detail.
 Used in Robotic applications
Autonomous navigation
Dynamic scene analysis
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26. Spatial Techniques
 Simple approaches for detecting changes between
two image frames (Compare two images pixel by
pixel)
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Frequency Domain Techniques
 Compute Fourier Transformation of the image

27. Question No 01 :
What is the most stable segmentation method?
Morphological Watersheds Segmentation.
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28. Question No 2 :
What are the pros and cons of region growing?
Pros
Better in noisy image where edges are hard to identify
Cons
Results depends on the selected seed point.
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29. Question No 03:
What is the purpose of use markers in watershed
segmentation?
Prevent Over Segmentation.
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Thank you!