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Chapter 1 - Imaging…read more

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1.1 Seeing Invisible Things
Although as humans we can only see the visible spectrum of electromagnetic
radiation we can use machines to see UV and infrared radiation, they can also
make an image from sound.
Ultrasound used for seeing inside the body uses high frequency sound waves
to make an image, a much higher frequency than human ears can detect.
Resolution is the smallest size object a sensor can detect. For pictures this the
size of the pixels, if you zoom in to far the image becomes pixelated.
In order for a sensor to show a decent image the wavelength has to be much
smaller than the object being detected, as this affects the resolution.
Formulae for waves used in imaging:
T=1 = vT = v
f f T = time of oscillation
v = velocity
f = frequency
= wavelength…read more

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1.1 Seeing Invisible Things
· To see very small things, such
as atoms, we would need to
use a wave with an extremely
short wavelength. Much
shorter than any of the
electromagnetic spectrum, so
instead we use electrons. An
scanning tunnelling micro
scope is a ultra sharp needle
point held just above a surface
so atoms, so close the
electrons can `tunnel' across
the gap, this creates a current
and the smaller the gap the
larger the current, and this
data can be used to create an
image of electrons.…read more

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1.2 Information in images
Bits and Bytes
· Information is stored digitally in binary form, this is a series of 0's
and 1's, one digit is called a bit. But in images we use 256 shades
of colour so we need 8 bits to store each pixel, 8 bits is 1 byte.
· N = 2I and log2(N)=I N = Number of alternatives
I = Amount of bit
· So N = 28 = 256
· This is normally shown on a logarithmic scale, one which equal
distances correspond to equal multiples
· Each shade of a colour is given a number given to each shade a
colour of either grey (for black and white) or RGB (for colour
pictures). The higher the number the darker the shade.
· This is pictures are stored and sent to people as the each pixel uses
3 bytes of storage…read more

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1.2 Information in images
Image Processing
· Most images contain noise which distorts the image, so changes the
data you can collect form it.
· To solve these problems we use of image processing
· Smoothing sharp edges: - replace each pixel by the mean of those
around it, (mean method).…read more

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