MATLAB (Matrix Laboratory)

CTRL-C

For those times when you forget to suppress several million lines of output.

pre filter post filter, weister filter ,mean filter meridian filter

Generating Sine and Cosine Signals (Use updated lab)

taking an example of matrix x ,we find whether this matrix is transitive or not: x=[1 1 0 ;1 0 1;1 0 1] m=1; for i=1:3 for j=1:3 if x(i,j)==1 for k=1:3 if x(j,k)==1 if x(i,k)~=1 m=0; end end end end end end if m==1 disp('Given matrix is Transitive') else disp('Given Matrix is not Transitive') end

swf is vector format, that means image can be re sized without loss of quality, but if you place bitmap or raster image in swf file when resized (enlarged) image will appear blurry.

many filters are available.

some are bilateral,trilateral, anisotropic,median etc

If you are referring to the dot function before multiplication and division, this bypasses the constraints of matrix operations so that you can simply divide the elements in same-dimensioned matrices element by element.

You can't get Matlab for free. Your best bet would be to go through your institution and hope they have a license.

You can go to the web of MathWork to get a trial version, it is free. If you are a student, you can buy a student version, it is not expensive.

1. Image acquisition is the first process. Generally the image acquisition stage involves preprocessing such as scaling.
2. Image enhancement is among the simplest and most area. The idea behind enhancement techniques is to bring out detail that is obscured or simply to highlight certain features of interest in an image.
3. Image restoration is ain area that also deals with improving the appearance of an image. Unlike enhancement, which is subjective, image restoration is objective. Image restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement on the other hand, is based on human subjective preferences regarding what constitutes a good enhancement result
4. Color image processing
5. Wavelets are the foundation for representing images in various degrees of resolution.
6. Compression deals with techniques for reducing the storage required to save an image or the bandwidth required to transmit it.
7. Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.
8. Segmentation procedures partition an image into its constituent parts or objects.
9. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region. Representation first deals with whether the data should be represented as a boundary or as a complete region. Choosing representation is only part of the solution for transformation raw data into a form suitable for subsequent computer processing. A method must also be specified for describe the data so that features of interest are highlighted. Description or feature slection deals with extracting attributes that result in some quantitative information of interest or are basic for differentiation one class of objects from another.
10. Object recognization

Google fills its index for image search by crawling the web. Upload your images to a web-site, wait for Google to re-index the site, and your images should show up in image search within a couple weeks.

No One Can Upload Images To Google Images,

Those Images Are Crawled By Google Robots.

One great way to have photos uploaded to Google Images is to save the file name just as you want Google to list it in Google Images.

Make sure this Image is then uploaded onto a website that is listed in Google Search Results.

When the spider searches the web page, Google will identify the image automatically and list it in Google Images.

There may be other ways, however, this is a good start.

image profiling is observing an individual's distinct actions/characteristics and being able to determine future actions based upon those movements

%%%fim1 is our image%%%

[ r c ] = size(fim1);

even=zeros(r,(c/2));

%first level decomposition

%one even dimension

for j = 1:1:r

a=2;

for k =1:1:(c/2)

even(j,k)=fim1(j,a);

a=a+2;

end

end

%one odd dim

odd=zeros(r,(c/2));

for j = 1:1:r

a=1;

for k =1:1:(c/2)

odd(j,k)=fim1(j,a);

a=a+2;

end

end

[ lenr lenc ]=size(odd) ;

%one dim haar

for j = 1:1:lenr

for k =1:1:lenc

fhigh(j,k)=odd(j,k)-even(j,k);

flow(j,k)=even(j,k)+floor(fhigh(j,k)/2);

end

end

%2nd dimension

[len2r len2c ]=size(flow);

for j = 1:1:(len2c)

a=2;

for k =1:1:(len2r/2)

%even separation of one dim

leven(k,j)=flow(a,j);

heven(k,j)=fhigh(a,j);

a=a+2;

end

end

%odd separtion of one dim

for j = 1:1:(len2c)

a=1;

for k =1:1:(len2r/2)

lodd(k,j)=flow(a,j);

hodd(k,j)=fhigh(a,j);

a=a+2;

end

end

%2d haar

[ len12r len12c ]=size(lodd) ;

for j = 1:1:len12r

for k =1:1:len12c

%2nd level hh

f2lhigh(j,k)=lodd(j,k)-leven(j,k);

%2nd level hl

f2llow(j,k)=leven(j,k)+floor(f2lhigh(j,k)/2);

%2nd level lh

f2hhigh(j,k)=hodd(j,k)-heven(j,k);

%2nd level ll

f2hlow(j,k)=heven(j,k)+floor(f2hhigh(j,k)/2);

end

end

% level=level-1;

The most recent one (which will vary depending on when the user reads this answer).

clc

clear all

close all

a = [1 2 3 4];

b = [6 7 8 9 10];

f=length(a);

g=length(b);

h=(f+g-1);

i=[a,zeros(1,(h-f))];

j=[b,zeros(1,(h-g))];

y1 = fft(i);

y2 = fft(j);

z = y1.*y2;

c = ifft(z);

subplot(2,2,1); plot(a)

title('a')

subplot(2,2,2); plot(b)

title('b')

subplot(2,2,3); plot(c)

title('Convolution of a,b')

The output as a result of IMAGE PROCESSING is an image ie a transformed image(enhanced) but in case of COMPUTER VISION the output is usually a decsion

http://www.ziddu.com/download/3304034/dip.pdf.html

The form of the piecewise functions can be arbitrarily complex, but higher degrees of specification require considerably more user input.

Matlab is a package that is available for licensed users. The program required for it is available at the official site of Matlab

Below is one simple code to generate unit impulse.

clc

close all

n1=-3;

n2=4;

n0=0;

n=[n1:n2];

x=[(n-n0)==0]

stem(n,x)

The resultant impulse will be

00010000 in a graphical manner.

The code below generates a saw tooth wave in Matlab

fs = 10000;

t = 0:1/fs:1.5;

x = sawtooth(2*pi*50*t);

subplot(1,2,1);

plot(t,x), axis([0 0.2 -1 1]);

xlabel('t'),ylabel('x(t)')

title('sawtooth signal');

N=2; fs = 500;n = 0:1/fs:2;

x = sawtooth(2*pi*50*n);

subplot(1,2,2);

stem(n,x), axis([0 0.2 -1 1]);

xlabel('n'),ylabel('x(n)')

title('sawtooth sequence');

The code below can generate triangular wave in Matlab.

A=2; t = 0:0.0005:1;

x=A*sawtooth(2*pi*5*t,0.25); %5 Hertz wave with duty cycle 25%

plot(t,x);

grid

axis([0 1 -3 3]);

x = linspace(-5,5);

y = sinc(x);

subplot(1,2,1);plot(x,y)

xlabel('time');

ylabel('amplitude');

title('sinc function');

subplot(1,2,2);stem(x,y);

xlabel('time');

ylabel('amplitude');

title('sinc function');

The code above can generate a sinc function

Initially, the equation can be directly realized using Matlab source code. Then various inputs can be applied to it. These values can easily be plotted on a graph using plot or stem command in Matlab.