Dip Image Segmentation
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Transcript of Dip Image Segmentation
1
Image Segmentation
Chapter 10
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Image Segmentation
Segmentation subdivides an image into its
constituent regions or groups.
The level to which the subdivision is carried depends
on the problem being solved.
That is, segmentation should stop when the objects
of interest in an application have been isolated.
e.g. automated inspection of electronic assemblies;
specific anomalies; missing components or broken
connection paths.
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Image Segmentation
Segmentation algorithms ; Two categories
based on two basic properties of intensity
values :
discontinuity and similarityFirst Category : Abrupt changes in intensity ;
edges
Second Category : partitionning of regions which
are similar according to a set of predefined criteria.
e.g. Thresholding, region growing, region splitting
and merging.
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Image Segmentation
First Category :
Points, Lines, Edges
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Detection of
discontinuities
Points, lines, edges
The most common way
R = w1*z1 + w2*z2 + ……+ w9*z9
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Point detection
R T
T = Threshold
Figure 10.2 (a) point detection mask
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Point detection
(b) X-ray image of a turbine blade with porosity
(c) Result of point detection mask
(d) Result of point detection mask with threshold
Figure 10.2
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Line detection
– A Suitable Mask in desired direction
– Thresholding
Figure 10.3 Line masks
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• Example:
Line detection
-45º Mask Thresholding
Figure 10.4 Illustration of line detection (a) ,(b),(c)
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Edge Detection
– Two Mathematical model
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Edge Detection
Second derivative
First derivative
Gray level profile
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Problem of Noise
Gaussian Noise (mean, sigma)
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Gradient Operators
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Gradient Operators
X-directionY-direction
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– Roberts Cross Gradients:
Gradient Operators
– Prewitt Operators:
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Diagonal Edge
– 45-Direction
45-Direction
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Gradient Operators
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Gradient Operators
Pre-
Smoothing
5×5
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Diagonal edge detection
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Laplacian as an isotropic Detector:
Discrete Implementation:
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Laplacian of Gaussian (LoG):
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Edge detection (overview)
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Image Segmentation
Second Category :
Thresholding, region growing, region splitting and merging
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Thresholding
– F(x,y)>T then (x,y) is belong to object, else (x,y) is belongto background.• Bi-level (T)• Multi-level (T1,T2,…, Tn)• Threshold image:
– Threshold Estimation :• Histogram
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Thresholding