Edge Detection Seminar

8/3/2019 Edge Detection Seminar

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ByNikhil JoyRoll No. 16

S7 EC BETA


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1) Introduction2) Edge Detection Algorithm3) Gradient Operator4) Commonly Used Operators5) Edge Detection Flow Diagram6) Applications

7) References8) Acknowledgement


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The ability to measure gray-level transitionsin a meaningful way.


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Edge detection refers to the process of

identifying and locating sharpdiscontinuities in an image.

The discontinuities are abrupt changes in

pixel intensity which characterizeboundaries of objects in a scene.


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ORIGINAL IMAGE AFTER EDGE DETECTION


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Most information in an image resides along

edges.

Preprocessing operation that narrows down

search in feature detection.

Filters out useless information, while

preserving the important structuralproperties in an image.


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Basic step for many important areas, such

as machine vision and automatedinterpretation systems.

Used as the front-end processing stage forhigher level object recognition andinterpretation systems.


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Classical methods :Involves measuring the gray level transitions.

Done by determining the gradient of theimage(first derivative).

If gradient exceeds a particular thresholdthen it is an edge.

If it does not exceed the threshold then edge notdetected.


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2.1 Grey Level Transitions

IDEAL RAMP


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2.2 The First Derivative

ORIGINAL FIRST DERIVATIVE


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2.3.1 Detecting the Edge (A)

yxI , x

yxI

,

TRSH

DetectedEdgeTRSH

x

yxI

,

ORIGINAL FIRST DERIVATIVE


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2.3.1 Detecting the Edge (B)

yxI ,

ORIGINAL

x

yxI

,

TRSH

DetectedNotEdgeTRSH

x

yxI

,


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3. Gradient Operators

The gradient of the image I(x,y) at location (x,y), isthe vector:

The magnitude of the gradient:

The direction of the gradient vector:

y

yxIx

yxI

G

GI

y

x

,

,

22 yx GGII

y

x

G

Gyx

1tan,


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3.1 The Meaning of the Gradient

It represents the direction of the strongest variationin intensity.

Horizontal Vertical

EdgeStrength:

EdgeDirection:

Generic

0,

yxGI x

2

,

yx

GIy

x

y

yx

G

Gyx

GGI

1

22

tan,


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For calculating the gradient there are anumber of Operators used.

Some of these operators are:

SOBEL Operator

ROBERTS Cross Operator

PREWITT Operator


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1 2 1

0 0 0

-1 -2 -1

-1 0 1

-2 0 2

-1 0 1

987321 22 zzzzzzGy 741963 22 zzzzzzGx


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0 0 0

0 1 0

0 0 -1

0 0 0

0 0 1

0 -1 0

95 zzGx 86 zzGy


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1 1 1

0 0 0

-1 -1 -1

-1 0 1

-1 0 1

-1 0 1

987321 zzzzzzGy 963741 zzzzzzGx


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The image is convolved with a Gaussianfilter before gradient evaluation.

It uses two thresholds, and enables thedetection of two edge types: strong andweak edge.


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ImageFile

Display

EdgeDetection

Script

RGBto

Grayscale


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Face detection

Automated interpretation systems

Machine vision


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Videoin

VideooutEdge

Detection Video ScreenCamera


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1. E. Argyle. Techniques for edge detection, Proc. IEEE, vol. 59, pp.

285-286, 1971

R.C. Gonzales & R. E. Woods Digital Image Processing, 2ndEdition, TATA McGraw Hill, 2001

J. Matthews. An introduction to edge detection: The sobel edgedetector, Available at:

http://www.generation5.org/content/2002/im01.asp, 2002.

J. F. Canny. A computational approach to edge detection. IEEETrans. Pattern Anal. Machine Intell., vol.PAMI-8, no. 6, pp. 679-697,1986


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I thank God Almighty for all His blessings. I wouldlike to express my profound gratitude to all thepeople who have inspired and motivated me toundertake this seminar.

I would like to thank Prof. Asha Panicker, Head ofthe Department for her unending support andencouragement. I am deeply indebted to her forproviding me with valuable advice and guidanceduring the course of the seminar.

I express my extreme gratitude and sincere thanksto our class teacher Mr. Sreeraj K.P. for for hissupport and guidance.

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Edge Detection Seminar