Image Final Print

8/2/2019 Image Final Print

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INDEX

Sr.No. Name of Experiment Date of

Starting

Date of

Completion

Signature

1 Introduction of MATLAB ( Basic

Commands)

2 To see the effect of change in

spatial resolution of Image

(Resizing , Zooming, Rotation,

Color map, Cropping)

3 Effect of Brightness Operation onan Image

4 Effect of adding and removing

noise in an Image ( Salt & Pepper

and Gaussian Noise ,Median and

Wiener Filters)

5 To prepare the Histogram of

different Images, Changes in Gray

level, Histogram Equalization

6 To pass the Image through Lowpass and High Pass Filters and to

see it s effect

7 To pass the Image through Band

pass and Band Reject Filters and to

see it s effect

8 To apply Edge, Pixel Detection

operation on Images

9 To see the Magnitude and Phase

information of an Image using FFTand IFFT

10 Mini Project Based on the

knowledge of MATLAB in Image

Processing

__________________________________________________________________________________FUNDAMENTALS OF IMAGE PROCESSING

(8th SEMESTER E.C.)R. K. College of Engineering & Technology, Rajkot

INDEX

FUNDAMENTALS OF IMAGE PROCESSING


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Experiment 1

AIM: - Introduction of MATLAB (Basic Commands)

MATLAB COMMANDS

a= [1 2 3; 4 5 6; 7 8 9]

a=

1 2 3

4 5 6

7 8 9

This forms 3 X 3 matrix labeled as a

b=a

b=

1 4 7

2 5 8

3 6 9

The transpose of matrix a is stored in matrix b

c=b*a

c=

66 78 90

78 93 108

90 108 126

Multiplies matrix b with a stores in c

c = a*b

c=

14 32 50

32 77 122

50 122 194Multiplies matrix a with b stores in c

a= [ 1 2 3 4 5]

a = 1 2 3 4 5

One dimensional array is defined as a




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b = a+3

b =

4 5 6 7 8

3 is added to each element of a

c = a+b

c =

5 7 9 11 13

Both arrays a and b are added and stored in c

eye(3)

ans =

1 0 0

0 1 00 0 1

Identical matrix formed

eye(5)

ans =

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

5 X 5 identical matrix formed

zeros(3,4)

ans =

0 0 0 0

0 0 0 0

0 0 0 0

3 x 4 matrix with all elements 0

ones(3,4)

ans =

1 1 1 1

1 1 1 1

1 1 1 1

3 x 4 matrix with all elements 1




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[eye(2);zeros(2)]

ans =

1 0

0 1

0 00 0

Identical matrix is appended with the 2 X 2 zero matrix

[eye(2),zeros(2)]

ans =

1 0 0 0

0 1 0 0

Identical matrix is appended with the 2 X 2 zero matrix and 2 X 4 matrix is

formed

a = [ 1 2 3;4 5 6;7 8 9]

a =

1 2 3

4 5 6

7 8 9

3 X 3 matrix is formed as a

a (2,1)a = 4

This gives the value of 2nd row and 1st element

a(1,2)

a = 2

This gives the value of 1st row and 2nd element

c = [ a; 10 11 12]

c =1 2 3

4 5 6

7 8 9

10 11 12

It appends a row in the matrix a




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[a; a; a]

ans =

1 2 3

4 5 6

7 8 91 2 3

4 5 6

7 8 9

1 2 3

4 5 6

7 8 9

This appends a to itself one after another and forms 9X3 matrix

[ a,a; a,a]ans =

1 2 3 1 2 3

4 5 6 4 5 6

7 8 9 7 8 9

1 2 3 1 2 3

4 5 6 4 5 6

7 8 9 7 8 9

This appends matrix a on the both directions and forms 6 X 6 matrix

f = [ -0.5, 0.1, 0.5]

f =

-0.5000 0.1000 0.5000

It defines a row matrix with non integer elements

round(f)

ans =

-1 0 1

It assigns the nearest integer value

f = [ -0.4,0.1,0.5]

f =

-0.4000 0.1000 0.5000

It defines a row matrix with non integer elements




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round(f)

f =

0 0 1

It assigns the nearest integer value

ceil (f)

ans =

0 1 1

It assigns the highest integer value

sum(f)

ans =

0.2000

Sum of all elements stored in matrix

fix(f)

ans =

0 0 0

The integer value is stored and the decimal value discarded

floor(f)

ans =

-1 0 0It stores the lower nearest integer value

[ 1,2,3,4].*[1,2,3,4]

ans =

1 4 9 16

It directly multiplies each elements of 1st matrix with 2nd matrix

[1,2,3,4] + [ 1,2,3,4]

ans =2 4 6 8

It directly adds each elements of 1st matrix with 2nd matrix

[1,2,3,4].^3

ans =

1 8 27 64

It makes cube of each elements of row matrix




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[1,2,3,4]*3

ans =

3 6 9 12

It multiplies each elements of row matrix with 3.

pi

ans =

3.1416

The value of p is displayed

eps

ans =

2.2204 e-016

The value of e is displayed

who

Your variables are:

a b e g

ans c f x

Displays all variables used

clear x

Your variables are:a b e g

ans c f

Clears the variable x

Clear all

Clears all the variables

x = -2: 1

x =-2 -1 0 1

It forms series of integer from 2 to 1 with unit diff.

length(x)

ans = 4

It gives the length of the series

t= 0:2:10




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t =

0 2 4 6 8 10

It gives series from 0 to 10 with difference of 2.

a =magic(3)a =

8 1 6

3 5 7

4 9 2

Forms 3X3 matrix with each raw and column having same sum

a (2,:)

ans =

3 5 7It displays 2nd raw with all elements

a(:,3)

ans =

6

7

2

It displays 2nd column with all elements

a(2:3:,:)

ans =

3 5 7

4 9 2

It displays 2nd to 3rd raw with all elements

b = rand(5)

ans =

0.9501 0.7621 0.6154 0.4057 0.0579

0.2311 0.4565 0.7919 0.9355 0.3529

0.6068 0.0185 0.9218 0.9169 0.8132

0.4860 0.8214 0.7382 0.4103 0.0099

0.8913 0.4447 0.1763 0.8963 0.1389

Generates a random 5X5 matrix




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c = 100 * rand(3)

c =

20.2765 27.2188 74.6786

19.8722 19.8814 44.509660.3792 1.5274 93.1815

It multiplies each elements of random matrix with 100.

[eye(2);zeros(2);zeros(2)]

ans =

0 0

0 1

0 0

0 00 0

0 0

[eye(2);ones(2)]

ans =

1 0

0 1

1 1

1 1

[eye(2);ones(2);zeros(2)]

ans=

1 0

0 1

1 1

1 1

0 0

0 0




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Experiment 2

AIM:- To see the effect of change in spatial resolution of

Image ( Resizing , Zooming, Rotation, Color map,Cropping, Movie)

Code

Clc;

Clear all;

Close all

Subplot (3,2,1);

a=Imread (('moon.tif')

Imshow (a);

Title ('Original Moon');

Subplot (3,2,2);

b=Imresize (a,[40 40]) % co ordinates are for y and x respectively

Imshow (b);

Title ('Moon represented using 40 x 40 Pixels');

Subplot (3,2,3);

c=imrotate (a,90);

Imshow (c);Title ('Moon Rotated by 90 degrees in ACW Direction');

Subplot (3,2,4);

e=imcrop (a,[10 10 240 230]);

Imshow (e);

Title ('Cropped Moon');

Subplot (3,2,5);

imcontour (a,4);Title ('Contours of Moon');

Subplot (3,2,6);

load mri;

immovie (D,map);

Title ('A 27 frame MRI Image');

figure;




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a=Imread (('moon.tif');

image(a);

Title ('Color mapped Moon');

Color map(jet); % Different color mapping can be used viz Fire, cool etc




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MATLAB Functions UsedImread :Syntax

A = Imread ((filename, fmt)

[X, map] = Imread ((filename, fmt)Description

A = Imread ((filename, fmt) reads a grayscale or color image from the file

specified by the string filename, where the string fmt specifies the format of the

file. Imread (returns the image data in the array.

Class Support

For most file formats, Imread (uses 8 or fewer bits per color plane to store

pixels.

Imshow:Syntax

Imshow (I, n)

Imshow (I, [low high])

Description

Imshow (I,n) displays the intensity image I with n discrete levels of gray. And

by omitting n, Imshow uses 256 gray levels on 24-bit displays, or 64 gray levels

on other systems.

Imshow (I,[low high]) displays I as a grayscale intensity image, specifying the

data range for I. The value low (and any value less than low) displays as black;the value high (and any value greater than high) displays as white.

Class Support

The input image can be of class logical, uint8, uint16, or double, and it must be

non sparse.

Imresize:

Syntax

B = imresize (A, m)

B = imresize (A, m, method)Description

B = imresize (A, m) returns an image B that is m times the size of A, using

nearest-neighbor interpolation. A can be an indexed image, grayscale image,

RGB, or binary image.

B = imresize (A, m, method) returns an image that is m times the size of A

using the interpolation method specified by method. Method is a string that can

have one of values viz. Nearest-neighbor interpolation, bilinear interpolation,

Bicubic interpolation.




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Class Support

The input image A can be numeric or logical and it must be non sparse. The

output image B is of the same class as the input image.

Imrotate:Syntax

B = imrotate (A, angle)

B = imrotate (A, angle, method)

Description

B = imrotate (A, angle) rotates the image A by angle degrees in a

counterclockwise direction, using the nearest-neighbor interpolation. To rotate

the image clockwise, specify a negative angle.

B = imrotate (A, angle, method) rotates the image A by angle degrees in acounterclockwise direction, using the interpolation method specified by method.

method is a string that can have one of theseClass Support

The input image A can be numeric or logical and it must be no sparse. The

output image B is of the same class as the input image.

imcrop:Syntax

I2 = imcrop (I)X2 = imcrop(X, map)

Description

imcrop crops an image to a specified rectangle. In the syntaxes below, imcrop

displays the input image and waits for you to specify the crop rectangle with the

mouse.

Class Support

The input image A can be of class logical, uint8, uint16, or double. The output

image B is of the same class as A. rect is always of class double.

Color map:Syntax

Color map (map)

Cmap = color map

Description

A color map is an m-by-3 matrix of real numbers between 0.0 and 1.0. Each

row is an RGB vector that defines one color. The kth row of the color map




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defines the kth color, where map(k,:) = [r(k) g(k) b(k)]) specifies the intensity of

red, green, and blue.

Specifying Color maps

There are total eleven types which specifies color like autumn, hsv, pink, prism,

summer and winter and some more. All these classification specifies its owndifferent intensity of color.

Immovie:Syntax

Mov = immovie(X,map)

Mov = immovie(RGB)

Description

Mov = immovie(X,map) returns the movie structure array mov from the images

in the multiframe indexed image X with the color map. As it creates the moviearray, it displays the movie frames on the screen.Class Support

A true-color image can be uint8, uint16, or double. Mov is a MATLAB movie

structure.

Imcontour:Syntax

Imcontour (I)

Imcontour (I,n)Description

Imcontour (I) draws a contour plot of the intensity image I, automatically

setting up the axes so their orientation and aspect ratio match the image.

Imcontour (I, n) draws a contour plot of the intensity image I, automatically

setting up the axes so their orientation and aspect ratio match the image. n is the

number of equally spaced contour levels in plot.

Class Support

The input image can be of class uint8, uint16, double, or logical.




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Experiment 3

AIM: - Effect of Brightening Operation on an Image

Code

Clc;

Clear all;

Close all;


Imshow (a);

Title ('Moon with Command Imshow ()');

Imview (a);

b=.5;

brighten(b)Title ('Moon with Brightness by argument 0.5');

b=-.5;

figure;

Imshow (a)

brighten (b)

Title ('Moon with Darkness by argument 0.5');

whos

imfinfo(moon.tif);

Command Window

Name Size Bytes Class

a 537x358 192246 uint8 array

b 1x1 8 double array

Grand total is 192247 elements using 192254 bytes




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MATLAB Functions UsedBrighten :Syntax

brighten (beta)

brighten (h, beta)Description

Brighten increases or decreases the color intensities in a Color map. The

modified Color map is brighter if 0 < beta < 1 and darker if -1 < beta < 0.

Brighten (beta) replaces the current Color map with a brighter or darker Color

map of essentially the same colors. Brighten (beta), followed by brighten (-

beta), where beta < 1, restores the original map.

Brighten (h, beta) brightens all objects that are children of the figure having the

handle h.




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Experiment 4

AIM :- Effect of adding and removing noise in an Image

( Salt & Pepper and Gaussian Noise ,Median and WienerFilters)

Code

Clc;

Clear all;

Close all;

a=Imread (('eight.tif');

Subplot (3,2,1);

Imshow (a);

Title ('Original Image');

Subplot (3,2,2);

b1=Imnoise (a,'salt & pepper');

Imshow (b1);

Title ('Image with Salt and Pepper Noise');

Subplot (3,2,3);

b2=Imnoise (a,'gaussian');

Imshow (b2);Title ('Image with Gaussian Noise');

Subplot (3,2,4);

c1=medfilt2(b1);

Imshow (c1);

Title ('Image passed through Median Filter');

Subplot (3,2,5);

c2=wiener2(b2);Imshow (c2);

Title ('Image passed through Wiener Filter');




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MATLAB Functions UsedImnoise:Syntax

j = Imnoise (I,type)

J = Imnoise (I,type,parameters)Description

j = Imnoise (I,type) adds noise of a given type to the intensity image I. Type is a

string that can have one of these values.

J = Imnoise (I,type,parameters) accepts an algorithm type plus additional

modifying parameters particular to the type of algorithm chosen. If you omit

these arguments, Imnoise uses default values for the parameters.

Values Description

'gaussian' Gaussian white noise

'localvar' Zero-mean Gaussian white noise with an intensity-dependent variance

'poisson' Poisson noise

'salt & pepper' On and off pixels

'speckle' Multiplicative noise

Class Support

I can be of class uint8, uint16, or double. The output image J is of the same

class as I. If I has more than two dimensions it is treated as a multidimensional

intensity image and not as an RGB image.

Median Filter:Syntax

B = medfilt2(A,[m n])

B = medfilt2(A)

Description

Median filtering is a nonlinear operation often used in image processing to

reduce "salt and pepper" noise. Median filtering is more effective than

convolution when the goal is to simultaneously reduce noise and preserve

edges.B = medfilt2(A,[m n]) performs median filtering of the matrix A in two

dimensions. Each output pixel contains the median value in the m-by-n

neighborhood around the corresponding pixel in the input image. medfilt2 pads

the image with 0's on the edges, so the median values for the points within [m

n]/2 of the edges might appear distorted.

B = medfilt2(A) performs median filtering of the matrix A using the default 3-

by-3 neighborhood.




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Class Support

The input image A can be of class logical, uint8, uint16, or double (unless the

'indexed' syntax is used, in which case A cannot be of class uint16). The output

image B is of the same class as A.

Wiener Filter:Syntax

J = wiener2(I,[m n],noise)

[J,noise] = wiener2(I,[m n])

Description

wiener2 lowpass-filters an intensity image that has been degraded by constant

power additive noise. wiener2 uses a pixelwise adaptive Wiener method based

on statistics estimated from a local neighborhood of each pixel.

Class SupportThe input image I is a two-dimensional image of class uint8, uint16, or double.

The output image J is of the same size and class as I.




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Experiment 5

To prepare the Histogram of different Images, Changes in

Gray level, Histogram Equalization

Code

Clc;

Clear all;

Close all;

a=Imread (('cameraman.tif');

Subplot (2,2,1);

Imshow (a);Title ('Original Image');

Subplot (2,2,2);

Imhist (a);

Title ('Histogram of Original Image');

c=Histeq (a);

Subplot (2,2,3);

Imshow (c);

Title ('Equalized Image');

Subplot (2,2,4);

Imhist (c);

Title ('Histogram of Equalized Image');




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MATLAB Functions UsedImhist:Syntax

Imhist (I, n)

Imhist (X, map)Description

Imhist (I) displays a histogram for the intensity image I above a grayscale color

bar. The number of bins in the histogram is specified by the image type. If I is a

grayscale image, Imhist uses a default value of 256 bins. If I is a binary image,

Imhist uses 2 bins.

Class Support

The input image can be of class logical, uint8, uint16, or double.

Histeq:Syntax

J = histeq (I,hgram)

J = histeq (I,n)

Description

Histeq enhances the contrast of images by transforming the values in an

intensity image, or the values in the color map of an indexed image, so that the

histogram of the output image approximately matches a specified histogram.

Class SupportFor syntaxes that include an intensity image I as input, I can be of class uint8,

uint16, or double, and the output image J has the same class as I.

uint8:Syntax

uint8 (a)Description

uint8 (a) returns the stored integer value of fi object a as a built-in uint8. If the

stored integer word length is too big for a uint8, or if the stored integer issigned, the returned value saturates to a uint8.




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Experiment 6

Aim:- To pass the Image through Low pass and High Pass

Filters and to see it s effect.

Code

Clc;

Clear all;

Close all;

a = Imread ((G:\Pooja\Wallpapers\CARTOONS\10.jpg');

Subplot (2,2,1)

Imshow (a);

Title ('Original Image');

[X,map] = rgb2ind(a,209); %Keep it below 256 otherwise blackout

%For Values between 0-25 approx also blackout

Subplot (2,2,2);

Imshow (X)

Title ('Indexed Image');

hl=(1/500)*ones(4,4);

hh=[-2 -2 -2 ; -2 -20 -2; -2 -2 -2];cl=filter2(hl,X,'valid');

Subplot (2,2,3);

Imshow (cl);

Title ('Image passed through LPF');

ch=filter2(hh,X,'valid');

Subplot (2,2,4);

Imshow (ch);

Title ('Image passed through HPF');




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MATLAB Functions Usedrgb2ind:

Syntax

X = rgb2ind(RGB, map)

Description

rgb2ind converts RGB images to indexed images using one of three different

methods: uniform quantization, minimum variance quantization, and Color map

mapping. For all these methods, rgb2ind also dithers the image unless you

specify 'no dither' for dither option.

filter2:

Syntax

Y = filter2(h, X)

Description

Filters the data in X with the two-dimensional FIR filter in the matrix h. It

computes the result, Y, using two-dimensional correlation, and returns the

central part of the correlation that is the same size as X.

Y = filter2(h, X, shape) returns the part of Y specified by the shape parameter.

shape is a string with one of these values:

'full' Returns the full two-dimensional correlation. In this case, Y is larger than

X.

'same'(default) Returns the central part of the correlation. In this case, Y is the

same size as X.

'valid Returns only those parts of the correlation that are computed without

zero-padded edges. In this case, Y is smaller than X.




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Experiment 7

AIM:- To pass the Image through Band pass and Band

Reject Filters and to see it s effect.

Clc;

Clear all ;

Close all ;

a = Imread (( 'G:\Pooja\Wallpapers\collection\abstract5.jpg' );

Subplot (3,2,1)

Imshow (a) ;

Title ( 'Original Image' ) ;[X,map] = rgb2ind(a,209) ; %Keep it below 256 otherwise blackout

%For Values between 0-25 approx also blackout

Subplot (3,2,2) ;

Imshow (X)

Title ( ' Indexed Image' ) ;

hl=(1/500)*ones(4,4) ;

hh=[ -2 -2 -2 ; -2 30 -2; -2 -2 -2];

cl=f ilter2(hl ,X, 'valid' ) ;

Subplot (3,2,3) ;

Imshow (cl ) ;

Title ( ' Image passed through LPF' ) ;

ch=f ilter2(hh,X, 'valid' ) ;

Subplot (3,2,4) ;

Imshow (ch) ;

Title ( ' Image passed through HPF' ) ;

Subplot (3,2,6) ;

bp=f ilter2(hl ,ch, 'valid' ) ;

Imshow (bp) ;

Title ( ' Image passed through BRF' ) ;

Subplot (3,2,5) ;

b1 = bp;

b2=0*b1;

br=b2-b1;




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Imshow (br ) ;

Title ( ' Image passed through BPF' ) ;




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Experiment 8

AIM:- To apply Edge, Pixel Detection operation on

Images.

Code for Edge Detection in an Image

Clc;

Clear all;

Close all;


Subplot (4,2,1);

Imshow (a);Title ('Original Moon');

b=edge(a,'prewitt');

Subplot (4,2,2);

Imshow (b);

Title ('Moon Edges with Prewitt');

c=edge(a,'canny');

Subplot (4,2,3);

Imshow (c);

Title ('Moon Edges with Canny');

e=edge(a, 'Zero cross');

Subplot (4,2,4);

Imshow (e);

Title ('Moon Edges with Zero cross');

f=edge(a, 'Zero cross');

Subplot (4,2,5);Imshow (f);

Title ('Moon Edges with Zero cross');

g=edge(a, 'sobel');

Subplot (4,2,6);

Imshow (g);

Title ('Moon Edges with Sobel');

h=edge(a, 'roberts');




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Subplot (4,2,7);

Imshow (h);

Title ('Moon Edges with Roberts');

i=edge(a,'log');Subplot (4,2,8);

Imshow (i);

Title ('Moon Edges with Laplacian');




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MATLAB Functions Usededge:

1)Sobel Method

Syntax

BW = edge(I, 'Sobel')

Description

The Sobel method finds edges using the Sobel approximation to the derivative.

It returns edges at those points where the gradient of I is maximum.

Class Support

I can be of class uint8, uint16, or double. BW is of class logical

2)Prewitt Method

SyntaxBW = edge(I,'prewitt')

Description

The Prewitt method finds edges using the Prewitt approximation to the

derivative. It returns edges at those points where the gradient of I is maximum.

Class Support

I can be of class uint8, uint16, or double. BW is of class logical.

3)Roberts Method

Syntax

BW = edge(I,'roberts')

Description

The Roberts method finds edges using the Roberts approximation to the

derivative. It returns edges at those points where the gradient of I is maximum.

Class Support


4)Laplacian of Gaussian MethodSyntax

BW = edge(I, 'log')

Description

The Laplacian of Gaussian method finds edges by looking for zero crossings

after filtering I with a Laplacian of Gaussian filter.

Class Support





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5)Zero-Cross Method

Syntax

BW = edge(I, 'Zero cross', thresh, h)

Description

The zero-cross method finds edges by looking for zero crossings after filtering Iwith a filter you specify

Class Support


6) Canny Method

Syntax

BW = edge (I, 'canny')

Description

The Canny method finds edges by looking for local maxima of the gradient of I.The gradient is calculated using the derivative of a Gaussian filter. The method

uses two thresholds, to detect strong and weak edges, and includes the weak

edges in the output only if they are connected to strong edges. This method is

therefore less likely than the others to be fooled by noise, and more likely to

detect true weak edges.

Class Support

I can be of class uint8, uint16, or double. BW is of class logical




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Experiment 9

AIM:- To see the Magnitude and Phase information of an

Image using FFT and IFFFT.

Clc;

Clear all;

Close all;

a = Imread ((moon.tif);

Subplot (3,2,1);

Imshow (a);Title (Original Image);

Subplot (3,2,2);

Imhist (a);

Title (Histogram of Original Image);

b=fft2(a);

Subplot (3,2,3);

Imshow (b);

Title (FFT of an Image);

c =ifft2(b);

d=uint8(c);

Subplot (3,2,4);

Imshow (c1);

Title (IFFT of an Original Image);

Subplot (3,2,5);

Imhist (d);Title ('Histogram of Restored Image');

e=Histeq (d);

Subplot (3,2,6);

Imshow (e);

Title ('Equalized Image');




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MATLAB Functions UsedFFT2:

Syntax

Y = fft2(X)

DescriptionY = fft2(X) returns the two-dimensional discrete Fourier transform (DFT) of X,

computed with a fast Fourier transform (FFT) algorithm. The result Y is the

same size as X.

Class Support

fft2 supports inputs of data types double and single. If you call fft2 with the

syntax y = fft2(X, ...), the output y has the same data type as the input X.

IFFT2:

SyntaxY = ifft2(X)

Description

Y = ifft2(X) returns the two-dimensional inverse discrete Fourier transform

(DFT) of X, computed with a fast Fourier transform (FFT) algorithm. The result

Y is the same size as X.

Class Support

ifft2 supports inputs of data types double and single. If you call ifft2 with the

syntax y = ifft2(X, ...), the output y has the same data type as the input X.




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MINI PROJECT

AIM:-

Image Final Print

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