WEL COME

C.V.RAMAN COLLEGE OF ENGINEERING

IMAGE SEGMENTATION

USING EDGE DETECTION

A SEMINAR ON

Presented By:

DILLIP KUMAR JYOTI7th Sem. ETC, Regd. No- 0701227175

Guided By:

Prof. B. MeherDept. of Electronics & Tele- communication

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Produced by Optical Device

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Image segmentation:

The process of partitioning a digital image into multiple regions or sets of pixels.

It is a psycho-physical problem.

Methods of segmentation:

Similarity detection technique.

• Region extraction

Discontinuity detection technique

• Point detection

• Line detection

• Edge detection

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Region Extraction:

Satisfies homogeneity property in image features over a large region.

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Edge Detection:

Detects abrupt change in image features within a small neighborhood.

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EDGE DETECTION:

Identifying & locating sharp discontinuities in an image.

It is used to obtain information from the frames for feature extraction and object segmentation.

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Categories: Derivative approach

Edges are detected by taking derivative followed by thresholding.

Pattern fitting approach- Edge templates over a small neighbourhood are analyzed.

- The best fitting function corresponding to the properties are determined by edge

filters.

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Derivative approach:

The backbone of many algorithms is the discrete approximation of derivative operations representing the significant gradient of intensity (edge).

First order derivativeSecond order derivative

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DERIVATIVE ( DIFFERENCE) OPERATOR

It yields high values at places where gray level changes rapidly , is used to find gradient of an image.

Where g(x,y) is a two dimensional function.

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Then rate of change in any direction is given by

where u is in the direction of

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1st Derivative

2nd Derivative

The direction in which rate of change has the greatest magnitude is,

And the magnitude of which is

The vector having this magnitude and direction is called the gradient of g(x, y), denoted by g’(x, y).IM

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GRADIENT OPERATOR

g(r-1, c-1) g(r-1, c) g(r-1, c+1)

g(r, c-1) g(r, c) g(r, c+1)

g(r+1, c-1) g(r+1, c) g(r+1, c+1)

The first differences instead of first derivative can be used for digital image.

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g2 g1 g8

g3 g0 g7

g4 g5 g6

≈

Magnitude of gradient:

Direction of the greatest steepness:

The gradient image g’(r, c) for the input image g(r, c) can be expressed as transformation:

An edge element is present at (r,c) if g’(r,c) exceeds a predefined threshold.

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1. SOBEL OPERATOR

Higher operators are assigned to the pixels close to the candidate pixel.

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0 0

2. ROBERTS OPERATOR

The Roberts Cross operator performs a simple, quick to compute, 2-D spatial gradient measurement on an image. It thus highlights regions of high spatial frequency which often correspond to edges. In its most common usage, the input to the operator is a grayscale image, as is the output.

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Similar weights are assigned to all the neighbours of the candidate pixel, g(r, c) whose edge strength is being calculated.

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N3. PREWITT OPERATOR

Kiresh operator represented by the templates:

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4. KIRESH OPERATOR

5. CANNY EDGE DETECTOR

Canny technique is very important method to find edges by isolating noise from the image before find edges of image, without affecting the features of the edges in the image.

• Good detection• Good localization• One response to one

edge.

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1.Convolve image g(r, c) with a Gaussian function to get smooth image g’(r, c) i.e.

g’(r, c)= g(r,c) * G(r,c; )

2. Apply first difference gradient operator to compute edge strength then edge magnitude and direction are obtain as before.

3. Apply non-maximal or critical suppression to the gradient magnitude.

4.Apply threshold to the non-maximal suppression image.IM

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The algorithmic steps for canny edge detection technique are as follows:

∂

The Laplacian of an image f(x, y) is a second order derivative defined as:

Sum of all the weights of the mask must add to zero. It suffers from the problem of false alarm.IM

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6. LAPLACIAN OPERATOR

7. EDGE MAXIMIZATION TECHNIQUE (EMT)

When there is more than one homogenous region (e.g. an image has many objects with different gray levels) or where there is a change on illumination between the objects and its background.

In this case, portion of the objects may be merged with the background or portions of the background may appear as an object.IM

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The complete process of generation of edge map may involve some or all of the following steps.

Noise smoothing

Edge Localization

Edge enhancement

Edge linking

Edge following

Edge extractionIMA

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CONCLUSION

In this paper, the comparative studies applied by using seven techniques of edge detection segment: Sobel, Roberts, Prewitt, Canny, Laplacian, Kiresh, and Edge Maximum Technique (EMT) on the Saturn original image.

A comparative study are explained & experiments are carried out for different techniques Kiresh, EMT and Prewitt techniques respectively are the best techniques for edge detection.

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THANK YOU