The document discusses various image segmentation techniques, including region growing, region splitting and merging, and morphological watershed segmentation. It explains the processes, advantages, and disadvantages of these methods, highlighting the importance of seed points in region growing and the use of markers in watershed segmentation to prevent over-segmentation. Additionally, it addresses motion-based segmentation and spatial techniques for detecting changes in images.

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!