Overview
This is a simple tutorial to get you started with Matlab. Matlab makes it easy to perform scientific computations without having to learn a programming language such as Fortran, C, or C++. This tutorial is developed keeping assignment_0.pdf for AM205 in mind. It can be used for other classes or self-learning as well.
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- Matlab online tutorial at: http://www.mathworks.com/academia/student_center/tutorials/mltutorial_launchpad.html?s_tid=int_tut
- Comprehensive online documentation at the Matlab site: http://www.mathworks.com/help/techdoc/index.html
- Another site: http://en.wikibooks.org/wiki/MATLAB_Programming
- Numerical Computing with MATLAB by Cleve Moler
- A Non-matlab, but very useful book: Numerical Recipes (2007). William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Cambridge University PressRecipes (2007). William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Cambridge University Press
Using Matlab
Opening Matlab:
This depends on your operating system. On Mac, it is usually installed in /Applications and you should see a Matlab icon with a MATLAB_<version number>. Double clicking the icon should open it. On Windows, there should either be a shortcut on the desktop or it can be found under programs in the start-up.
Matlab Development Environment (Matlab Desktop):
This can change with new releases. The following video gives a quick introduction:
http://www.mathworks.com/videos/working-in-the-development-environment-69021.html?s_cid=learn_vid
The key items are "Command Window" (where you type commands in this tutorial), "Current Folder", and the "Workspace".
Getting Help
Note: In Matlab, any line beginning with % is a comment (not executed by matlab). Lines without % are commands that are executed by matlab. You can copy and paste the matlab statements in this tutorial into matlab
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function [m,val] = cheby(k,x)
% Chebyshev polynomial recurrent relation
% Relevant to question 2 in assignment_0
% Note: x and k are not vectors.
if k==0 % if ... elseif.. block controls the flow
% based on the value of k in this example.
m=0;
val=1.0;
elseif k==1
m=1;
val=x;
elseif k>=2
tch(1)=1;
tch(2)=x;
for i=3:k+1
tch(i)=2*x*tch(i-1)-tch(i-2);
end
m=i-1;
val=tch(i);
end
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%%The primary functions for loading images is 'imread' and for displaying images is 'imshow'.
im=imread('baboon.png');
imshow(im);
%% Images are basically two dimensional array of pixels (picture elements) with a third dimension
%% that shows the intensity of pixel. If the image is a gray scale image,, the intensity is a single number
%% at each pixel. If it is a color image, it is stored as a "third dimension" with 3 value
%% each corresponding to the intensity of r (red), g (green) and b (blue) colors. Thus if we do:
size(im)
%% The result is 512 512 3, which indicates it is a two dimensional array of 512 x 512 pixels with three colors.
%% To get the 'red' intensity, you can do:
r=im(:, :, 1);
%% This is now a two dimensional array. The color intensity can vary anywhere from 8 bits (256 levels)
%% for grayscale to 24 bits (8 bits for each color) for color images. There are even 30-, 36-, or 48- bit
%% images. As far as Matlab is concerned,. the image once loaded is a three dimensional array with the last
%% dimension taking on only 3 values.
max(r(:)),min(r(:))
%% will show 255 and 0 (the red color is stored in 8 bits).
imshow(r)
%% will show the image in grayscale with intensity corresponding to "red" color intensity in the
%% original image. You can convert the color image to a grayscale image:
img=rgb2gray(im);
imshow(img)
This is now a two dimensional image with a grayscale intensity of 8 bits. We have compressed the image
at the expense of color! |
Grayscale Images:
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%% Many medical images (such as MRI, CT, ultrasound, etc.) are likely to be grayscale images.
%% They can 10 or 12 bit images and it is convenient to represent them using 16 bits as computers can 16 bits efficiently.
A=imread('Brain-MRI.jpg');
size(A)
imshow(A)
%% We can convert this to an grayscale image with:
B=rgb2gray(A)
%% This will be a 2D array of pixels with gray shades represented by 8 bits (256 shades).
%% We can plot a histogram of color intensity (x-axis intensity, y axis -- number of pixels) with:
imhist(B)
%% We can reduce the variation in the intensity among the pixels (and thereby visually enhance the darker regions)
%% by using the matlab function histeq.
histeq(B)
%%will display the image with formerly darker regions more bright and brighter regions darker. |