Ijcet 06 10_001

http://www.iaeme.com/IJCIET/index.asp 1 [email protected]

International Journal of Computer Engineering & Technology (IJCET)

Volume 6, Issue 10, Oct 2015, pp. 01-12, Article ID: IJCET_06_10_001

Available online at

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=6&IType=10

ISSN Print: 0976-6367 and ISSN Online: 0976–6375

© IAEME Publication

___________________________________________________________________________

COMPARISON ANALYSIS OF SENSITIVITY

OF NOISE B/W VARIOUS EDGE

DETECTION TECHNIQUE BY ESTIMATING

THEIR PSNR VALUE

Reena Jangra

M. Tech student in C.E, IIET (JIND) under KUK, India

Abhishek Bhatnagar

Asstt. Prof. in C.S.E, IIET (JIND) under KUK, India

ABSTRACT

Edge (corner) are boundaries (borders) between different textures. Edge

(corner) also may be defined as discontinuities in image (picture) intenseness

from one pixel (picture element) to other. Edge (corner) for a image (picture)

are always important characteristics that recommend a suggestion for a

higher frequency. Edge (corner) detection (identification) is a image (picture)

processing technique for finding boundaries (borders) of objects within

images (pictures). It works as a result of detecting discontinuation in

brightness (clarity). Edge (corner) detection (identification) is used for image

(picture) segment & data mining in areas such as image (picture) processing,

computer vision, & machine vision. Common edge (corner) detection

(identification) algorithms include Sobel, Canny, Prewitt, Roberts & fuzzy

logic methods.

Key words: Edge Detection, Canny Edge Detection, Sobel, Robert, Prewit,

PSNR.

Cite this Article: Reena Jangra and Abhishek Bhatnagar. Comparison

Analysis of Sensitivity of Noise B/W Various Edge Detection Technique by

Estimating Their PSNR Value. International Journal of Computer

Engineering and Technology, 6(10), 2015, pp. 01-12.

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=6&IType=10

1. INTRODUCTION

The points at which image (picture) brightness (clarity) changes sharply are typically

structured into a set of curved line sectors termed edge (corner). same problem of

finding discontinuation in 1D signals is known as step detection (identification) &

problem of finding signal discontinuation with the time is known as change detection

Reena Jangra and Abhishek Bhatnagar


(identification). Edge (corner) detection (identification) is a fundamental tool in image

(picture) processing, particularly in areas of feature detection (identification) &

feature extraction.

Edge (corner) can be identity in a image by detecting correlation b/w image pixel

neighbor.

If a pixel (picture element)’s gray-level value is related to those around it, there is

most likely not a edge (corner) at that point.

If a pixel (picture element)’s has neighbors with extensively irregular gray levels, it

may close to at edge (corner) point.

Figure 1 [Original image ]

2. EDGE DETECTIONMETHODS

May are implemented with sector mask & based on distinct approximations to

differential operators.

Differential operations measure rate of change in image (picture) brightness (clarity)

function.

Some operators are return orientation information & others return information about

the existence of a edge (corner) at each point.

2.1 Roberts Operator

Mark edge(corner) point only

No information about edge(corner) orientation

Work most excellent with binary images(pictures)

Primary disadvantage:

High responsive to noise

A small number of pixel(picture element)s are used to approximate gradient

Comparison Analysis of Sensitivity of Noise B/W Various Edge Detection Technique by

Estimating Their PSNR Value


Implementation of Robert

a1=imread('tulips.jpg');

b1=im2double(a);

[m1,n1]=size(a);

L(1:m1,1:n1)=0

for i=1:m1-2;

for j=1:m1-2;

L(i,j)=-1*b1(i,j)+0+0+1*b1(i+1,j+1);

end;

end;

M(1:m1,1:n1)=0

for i=1:m1-2;

for j=1:m1-2;

M(i,j)=0-1*b1(i,j+1)+1*b1(i+1,j)+0;

end;

end;

figure;

subplot(2,2,1)

imshow(L)

title('Robert Gx1');

subplot(2,2,2)

imshow(M)

title('Robert Gy1');

N=M+L;

subplot(2,2,3)

imshow(N)

title('Robert Gx1+Gy1');

subplot(2,2,4)

imshow(b1)

title('Original Image');



Figure 2 [Edge Detection using Robert]

2.2 Prewit Operator

The masks are as follows

Edge(corner) Magnitude =

Edge(corner) Direction =

Implementation of prewit

%PREWIT

N(1:m1,1:n1)=0

for i=1:m1-2;

for j=1:m1-2;

N(i,j)=-1*b(i,j)-1*b(i,j+1)-

1*b(i,j+2)+0+0+0+1*b(i+2,j)+1*b(i+2,j+1)+1*b(i+2,j+2);

end;

end;

O(1:m1,1:n1)=0

for i=1:m1-2;

for j=1:m1-2;

22 yx

x

y1tan

111

000

111

y

101

101

101

x




O(i,j)=-1*b(i,j)+0+1*b(i,j+2)-1*b(i+2,j)+0+1*b(i+1,j+2)-

1*b(i+2,j)+0+1*b(i+2,j+2);

end;

end;

figure;

subplot(2,2,1)

imshow(N)

title('Prewit Gx1');

subplot(2,2,2)

imshow(O)

title('Prewit Gy1');

Z=N+O;

subplot(2,2,3)

imshow(Z)

title('Prewit Gx1+Gy1');

subplot(2,2,4)

imshow(b)


Figure 3 [Edge detection using prewit]



2.3 Sobel Operator

Looks for edge (corner) in both horizontal & vertical directions, then merge

information into a single particular metric.

The masks are as follows:

Edge(corner) Magnitude =

Edge(corner) Direction=

Implementation of sobel

%SOBEL

P(1:m1,1:n)=0

for i1=1:m1-2;

for j=1:m1-2;

P(i1,j)=-1*b(i1,j)-2*b(i1,j+1)-

1*b(i1,j+2)+0+0+0+1*b(i1+2,j)+2*b(i1+2,j+1)+1*b(i1+2,j+2);

end;

end;

R(1:m1,1:n)=0

for i1=1:m1-2;

for j=1:m1-2;

R(i1,j)=-1*b(i1,j)+0+1*b(i1,j+2)-2*b(i1+1,j)+0+2*b(i1+1,j+2)-

1*b(i1+2,j)+0+1*b(i1+2,j+2);

end;

end;

figure;

subplot(2,2,1)

imshow(P)

title('Sobel Gx1');

subplot(2,2,2)

imshow(R)

title('Sobel Gy1');

22 yx

x

y1tan

121

000

121

y

101

202

101

x




Y=P+R;

subplot(2,2,3)

imshow(Y)

title('Soble Gx1+Gy1');

subplot(2,2,4)

imshow(b)


Figure 4 [Edge detection using Sobel Operator]

3. PROPOSED WORK

Canny edge (corner) detector have highly developed algorithm originate from

previous work of Marr & Hildreth. It is a best possible edge (corner) detection

(identification) technique as offer good detection (identification), clear response &

good localization.

It is widely used in modern image (picture) processing techniques with a great or

advance achievement in this field. Objective of research is to High light benefit of

canny edge (corner) detection (identification) over traditional edge (corner) detection

(identification) schemes.

On analyzing all these edge (corner) detection (identification) techniques , it is

found that canny gives best possible edge (corner) detection (identification)

.Following are some points throwing light on reward of canny edge (corner) detector

as compared to additional or others detectors discussed here in this research:



Figure 5 [Canny based Edge detection]

4. OBJECTIVE OF RESEARCH

1. Less Sensitive to noise: As compared to others operators like Prewitt, Robert &

Sobel canny edge (corner) detector is less responsive to noise. Its uses Gaussian filter

that may removes noise at a large level as compared to different filters. LoG operator

is also greatly responsive to noise as discriminate twice in contrast to canny operator.

2. Remove streaking problem: standard operators’ like Robert uses only one

thresholding technique but its consequences is that it outcome the edge detection into

streaking. Streaking means, if edge (corner) gradient just above & just below set

threshold limit it removes valuable part of connected edge (corner), & leave

disconnected final edge (corner).

3. Adaptive in nature: slandered operators have unchanging kernels so may not be

adapted to a given image (picture) while performance of canny algorithm depends

only on variable or adjustable.

5. PEAK NOICE RATIO

The psnr function implements following equation to calculate Peak Signal-to-Noise

Ratio (PSNR):

PSNR=10log10

(peak val2/MSE)

where peak val is either specified by user or taken from rage of image(picture) data

type

function psnr= PSNR(X1,Y1)

%Calculates Peak-to-peak Signal to Noise Ratio of two images(pictures) X & Y

[M1,N1]=size(X1);

m1=double(0);

X1=cast(X1,'double');




Y1=cast(Y1,'double');

for i1=1:M1

for j1=1:N1

m1=m1+((X1(i1,j1)-Y1(i1,j1))^2);

end

end

m1=m1/(M1*N1);

psnr1=10*log10(255*255/m1);

psnr1

5.1 ROBERT

>>imgr=imread(‘Robert.jpg’)

5.2 PREWIT

>>imgr=imread(‘prewit.jpg’)



5.3 SOBEL

>>imgs=imread(‘sobel.jpg’)

5.4. OLD CANNY (SINGLE THRESHOLD)

>>imgoc=imread(‘oldcanny1.jpg’)




5.5 CANNY (DOUBLE THRESHOLD)

6. TABLE OF PSNR ANALYSIS WITH DIFFERENT

TECHNIQUE

Technique Test1 Test2 Test3

Robert 20.0619 19.981 20.0729

Prewit 20.4084 20.4544 19.9521

Sobel 20.3162 20.4245 20.4147

Oldcanny 20.0191 20.0229 20.0354

canny 20.0816 20.0886 20.0959

Graph: Comparison analysis b/w all technique



7. FUTURE SCOPE & CONCLUSION

In this research we have studied & calculate different edge (corner) detection

(identification) techniques. We observed that canny edge (corner) detector offer a

confident positive result as compared to others with several optimistic points.

It is a lesser amount of responsive to noise, frank with nature, resolute dilemma of

streaking, offer good localization & detects sharper edge (corner) than others. It is act

as most favorable edge (corner) detection (identification) technique hence a big

amount of work & upgrading on this algorithm is continue &has been done so

advance development are probable in future as a enhanced canny algorithm may

distinguish edge (corner) in color image (picture) without transforming in gray image,

improved canny algorithm for automatic mining (extraction) of moving (unstable)

object in image (picture) guidance.

It finds realistic application in Runway Detection (identification) & Tracking for

Unmanned Aerial Vehicle, in brain MRI image, cable insulation layer measurement,

Real-time facial look identification, edge (corner) detection (identification) of river

regime, Automatic several Faces Tracking & Detection(identification).

Canny edge (corner) detection (identification) technique is helpful in license plate

restructuring system that act as a significant component of intelligent traffic system

(ITS), finds convenient application in traffic organization, public protection &

military sector. In addition it finds fur there additional application & researches in

field of medical area as in ultrasound, x –rays etc.

REFERENCES

[1] The Technology of Night Vision by Harry P. Montoro, ITT Night Vision

http://www.photonics.com/EDU/Han dbook.aspx?AID=25144

[2] A. Marion An Introduction to image Processing, Chapman and Hall, 1991

[3] Azeema Sultana, Dr. M. Meenakshi, “Design and Development of FPGA based

Adaptive Thresholder for image Processing Applications” ,on line access

[4] Gerhard X. Ritter; Joseph N. Wilson, “ Handbook of Computer Vision

Algorithms in image Algebra” CRC Press, CRC Press LLC ISBN:0849326362

Pub Date: 05/01/96

[5] N. Nacereddine, L. Hamami, M. Tridi, and N. Oucief, “Non-Parametric

Histogram-Based Thresholding Methods for Weld Defect Detection in

Radiography “, online access.

[6] Otsu,N., "A Threshold Selection Method from Gray-Level istograms,"IEEE

Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.

[7] Elham Ashari , Richard Hornsey, “ FPGA Implementation of Real-Time

Adaptive image Thresholding” ,online access

[8] http://en.wikipedia.org/wiki/Digital_image_processing

[9] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern

Analysis and Machine Intelligence, PAMI-8, 6, November 1986, 679–698.

[10] Ms. Sonali Meghare and Prof. Roshani Talmale, Developing and Comparing an

Encoding System Using Vector Quantization & Edge Detection. International

Journal of Computer Engineering and Technology, 4(3), 2013, pp. 503 – 511.

http://www.photonics.com/EDU/Han%20%20dbook.aspx?AID=25144