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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