it is based on region Based Method

1. Region Based
method
1

2. Contents
2
SL.NO TOPIC PAGE NO.
1. Image segmentation 4
2. Edge Detection 5
3. Intensity Histogram 6
4. Region Growing 7-9
5. Advantage and Disadvantage of
Region Growing.
10-11
6. Split and Merge Approach 12
7. Example 13
8. Split and Merge Algorithm 14-16
9. Region Splitting and Merging 17-21
10. Region Oriented Segmentation 22
11. conclusion 23

3. Image Segmentation:
• Segmentation refers to the process of partitioning a digital image into
multiple regions (sets of pixels).
• The goal of segmentation is to simplify or change the representation of
an
image into something that is more meaningful and easier to analyze.
• Image segmentation is typically used to locate objects and boundaries
in
images
• Each of the pixels in a region are similar with respect to some
characteristic or
computed property, such as color, intensity, or texture.
3

4. Edge Detection
4
• Edges in images are areas with strong intensity
contrasts – a jump in intensity from one pixel to the
next.
• Edge detecting an image significantly reduces the
amount of data and filters out useless
information, while preserving the important
structural properties in an image.
• There are many ways to perform edge detection.
– Gradient - The gradient method detects the edges by
looking for
the maximum and minimum in the first derivative of the
image.
– Laplacian - The Laplacian method searches for zero
crossings in the second derivative of the image to find
edges.

5. Intensity histograms provide a means of determining
useful intensity values as well as determining
whether or not an image is a good candidate for
thresholding or stretching.
Intensity histogram based
segmentation
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6. Region Growing
6
• Region growing is a procedure that groups pixels or sub regions into
larger regions.
• The simplest of these approaches is pixel aggregation, which starts
with a set of “seed” points and from these grows regions by
appending to each seed points those neighboring pixels that have
similar properties (such as gray level, texture, color, shape).
• Region growing based techniques are better than the edge-based
techniques in noisy images where edges are difficult to detect

7. Originalfigure
7
The Seed Points
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8. Result of regiongrowing
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Boundaries of segmented defective welds

9. THE ADVANTAGES AND DISADVANTAGES OF
REGION GROWING
9
separate the
Advantages
Region growing methods can correctly
regions that have the same properties we define.
Region growing methods can provide the original
images which have clear edges with good segmentation results.
The concept is simple. Weonly need a small number of seed points to represent
the property we want, then grow the region.

10. Disadvantage
10
• Computationally expensive
• It is a local method with no global view of the problem.
• Sensitive to noise.
• Unless the image has had a threshold function applied to
it, a continuous path of points related to color may exist
which connects any two points in the image.

11. Split and Merge Approach:
• This is a 2 step procedure:
– top-down: split image into
homogeneous quadrant
regions
– bottom-up: merge similar
adjacent regions
• The algorithm includes:
Top-down
– successively subdivide image into
quadrant regions Ri
– stop when all regions are
homogeneous: P(Ri ) = TRUE)
obtain quadtree structure
Bottom-up
– at each level, merge adjacent
regions
Ri and Rj if P(Ri [ Rj ) = TRUE
• Iterate until no further
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12. Example
12

13. The Split-and-Merge Algorithm
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1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0
Sample
image
1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0
First
split

14. 1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0
Second
split
1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0
14
Third
split

15. Merg
e
1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0
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Final
result
1 1 1 1 1 1 1 2
1 1 1 1 1 1 1 0
3 1 4 9 9 8 1 0
1 1 8 8 8 4 1 0
1 1 6 6 6 3 1 0
1 1 5 6 6 3 1 0
1 1 5 6 6 2 1 0
1 1 1 1 1 1 0 0

16. REGION SPLITTING AND MERGING
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Region Splitting
• Region growing starts from a set of seedpoints.
• An alternative is to start with the whole image as
a single region and subdivide the regions that
do not satisfy a condition of homogeneity.

17. Region Merging
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• Region merging is the opposite of region splitting.
• Start with small regions (e.g. 2x2 or 4x4 regions) and merge
the regions that have similar characteristics (such as gray level,
variance).
• Typically, splitting and merging approaches are used iteratively
CONTU…
…….

18. • Let R represent the entire image region and select a predicate .
• One approach for segmenting R is to subdivide it successively
into smaller and smaller quadrant regions so that , for Ri , P(Ri)
= TRUE.
• If P(R)FALSE divide the image into quadrants .
• If P is FALSE for any quadrant , subdivide that , quadrants and
so on.
• This particular splitting technique has a convenient
representation in the form called quad tree.
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19. R
R
2
R
1
R4
4
R4
2
R4
1
R4
3
R
3
R
4
Partitioned
image
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Corresponding quad tree

20.  Split into four disjoint quadrants any region Ri for which
P(Ri)=FALSE.
 Merge any adjacent regions Rj and Rk for which P(Rj U Rk) =
TRUE.
 Stop when no further merging or splitting is possible.
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21. REGION-ORIENTED SEGMENTATION
(a)Original image
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(b)Result of split and
merge procedure
(c)Result of thresholding in a

22. CONCLUSION
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• Region and boundary information for the purpose of segmentation.
• Image segmentation is an essential step in most automatic graphic pattern
recognition and scene analysis problems.
• One segmentation technique over another is dictated mostly by the peculiar
characteristics of problem beingmeasured.

23. Thank You!!
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