Region Splitting and Merging Technique For Image segmentation.

A broad view on image segmentation By spliiting and merging approach is discussed in this presentation .Kindly have a look on it!

1. Region Splitting and
Merging Technique for
Segmentation.
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2. Group Members
SwarnadeepModak(ECE2015/050)
SuvojitSanyal(ECE2015/051)
Somit Samanto(ECE2015/058)
SandipanRoy(ECE2015/40)
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3. Contents
SL.NOTOPIC 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. Splitand Merge Approach 12
7. Example 13
8. Split and Merge Algorithm 14-16
9. Region Splitting andMerging 17-21
10. RegionOriented Segmentation 22
11. conclusion 23
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4. ImageSegmentation:
•Segmentation refers to the process of partitioning a digital image into
multiple regions (sets ofpixels).
•The goal of segmentation is to simplify or change the representation of an
image into something that is more meaningful and easier toanalyze.
•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, ortexture.
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5. EdgeDetection
•Edges in images are areas with strong intensity contrasts –
a jump in intensity from one pixel to thenext.
•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 edgedetection.
–Gradient-The gradient method detects the edges by looking for
the maximum and minimum in the first derivative of theimage.
–Laplacian-The Laplacian method searches for zero crossings in
the second derivative of the image to findedges.
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6. 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 orstretching.
Intensity histogram based
segmentation
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7. Region Growing
•Regiongrowingisaprocedurethatgroupspixelsorsubregionsinto
largerregions.
•Thesimplestoftheseapproachesispixelaggregation,whichstarts
withasetof“seed”pointsandfromthesegrowsregionsby
appendingtoeachseedpointsthoseneighboringpixelsthathave
similarproperties(suchasgraylevel,texture,color,shape).
•Regiongrowingbasedtechniquesarebetterthantheedge-based
techniquesinnoisyimageswhereedgesaredifficulttodetect
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8. 7
Originalfigure The SeedPoints
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9. Result of regiongrowing
Boundaries of segmenteddefective welds
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10. THE ADVANTAGES AND DISADVANTAGESOF
REGIONGROWING
Advantages
Regiongrowingmethodscancorrectlyseparatethe
regions that have the same properties wedefine.
Regiongrowingmethodscanprovidetheoriginal
images which have clear edges with good segmentationresults.
The concept is simple. We only need a small number of seed points to represent
the property we want, then grow theregion.
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11. Disadvantage
•Computationallyexpensive
•Itisalocalmethodwithnoglobalviewoftheproblem.
•Sensitivetonoise.
•Unlesstheimagehashadathresholdfunctionappliedto
it,acontinuouspathofpointsrelatedtocolormayexist
whichconnectsanytwopointsintheimage.
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12. Split and MergeApproach:
•This is a 2 step procedure:
–top-down: split image into
homogeneous quadrantregions
–bottom-up: merge similar adjacent
regions
•The algorithm includes:
Top-down
–successively subdivide image into
quadrantregionsRi
–stop when all regions are
homogeneous:P(Ri )=TRUE)obtain
quadtreestructure
Bottom-up
–at each level, merge adjacentregions
Ri andRj ifP(Ri[Rj )=TRUE
•Iterate until no further
splitting/merging ispossible
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13. EXAMPLE
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14. The Split-and-MergeAlgorithm
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Sampleimage
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Firstsplit
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15. 11111112
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Secondsplit
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Thirdsplit
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16. Merge
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Finalresult
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17. REGION SPLITTING ANDMERGING
RegionSplitting
•Regiongrowingstartsfromasetofseedpoints.
•Analternativeistostartwiththewholeimageasa
singleregionandsubdividetheregionsthatdonot
satisfyaconditionofhomogeneity.
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18. RegionMerging
•Regionmergingistheoppositeofregionsplitting.
•Startwithsmallregions(e.g.2x2or4x4regions)andmerge
theregionsthathavesimilarcharacteristics(suchasgraylevel,
variance).
•Typically,splittingandmergingapproachesareused
iteratively
CONTU……….
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19. •LetRrepresenttheentireimageregionandselectapredicate.
•OneapproachforsegmentingRistosubdivideitsuccessively
intosmallerandsmallerquadrantregionssothat,forRi,
P(Ri)=TRUE.
•If P(R)FALSE divide the image into quadrants.
•If P is FALSE for any quadrant , subdivide that , quadrants and
soon.
•Thisparticularsplittingtechniquehasaconvenient
representation in the form called quadtree.
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20. R
R2R1
R44R42R41 R43
R3 R4
Partitioned
image
Corresponding quadtree
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21. SplitintofourdisjointquadrantsanyregionRiforwhich
P(Ri)=FALSE.
Merge any adjacent regions Rj and Rk for which P(Rj U Rk) =
TRUE.
Stop when no further merging or splitting ispossible.
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22. REGION-ORIENTEDSEGMENTATION
(a)Originalimage(b)Result of splitand
mergeprocedure
(c)Result of thresholding ina
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23. CONCLUSION
•Region and boundary information for the purpose ofsegmentation.
•Image segmentation isanessentialstepinmostautomatic graphic pattern
recognition and scene analysisproblems.
•One segmentation technique over another is dictated mostly by the peculiar
characteristics of problem beingmeasured.
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24. Thank You!!
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