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plot(-5,5,'*')
hold on
G=[2 6 5 3 2; 1 1 3 3 1;1 1 1 1 1];
plot(G(1,:),G(2,:),'b')
T=[1 0 5;0 1 -5;0 0 1];
G1=T*G;
plot(G1(1,:),G1(2,:), 'r')
R=[cos(pi/4) -sin(pi/4) 0;sin(pi/4) cos(pi/4) 0;...
0 0 1];
G2=R*G1;
plot(G2(1,:),G2(2,:),'g')
G3=inv(T)*G2;
plot(G3(1,:),G3(2,:),'k')
axis([-12 12 -12 12])
axis square
hold off

9.3 Manipulation of 2-D Images

Currently more and more images are being stored or transmitted in digital
form. What does this mean?
To simplify the discussion, consider a black and white image and assume
that it has a square boundary. The digital image is constructed by the optics
of the detecting system (i.e., the camera) to form on a plane containing a 2-D
array of detectors, instead of the traditional photographic film. Each of these
detectors, called a pixel (picture element), measures the intensity of light fall-
ing on it. The image is then represented by a matrix having the same size as
the detectors’ 2-D array structure, and such that the value of each of the
matrix elements is proportional to the intensity of the light falling on the
associated detector element. Of course, the resolution of the picture increases
as the number of arrays increases.

9.3.1 Geometrical Manipulation of Images
Having the image represented by a matrix, it is now possible to perform all
kinds of manipulations on it in MATLAB. For example, we could flip it in
the left/right directions (fliplr), or in the up/down direction (flipud),
or rotate it by 90° (rot90), or for that matter transform it by any matrix
transformation. In the remainder of this section, we explore some of the

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