Digital Image Processing: Digital Imaging ... - WordPress.com

companyscourgeΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

51 εμφανίσεις

Chapter
2

2012

Teacher:
Remah

W. Al
-
Khatib

This lecture will cover:


The human visual system



Light and the electromagnetic spectrum


Image representation


Image sensing and acquisition



Sampling,
quantisation

and resolution


The best vision model we have!



It is one of the most sophisticated image
processing and analysis systems.



Knowledge of how images form in the eye
can help us with processing digital images


Its understanding would also help in the
design of efficient, accurate and effective
computer/machine vision systems.

In the following slides we will consider what is
involved in capturing a digital image of a real
world scene:



Image sensing and representation



Sampling and
quantisation



Resolution



A typical image formation system consists of an
illumination” source, and a sensor.



Energy from the illumination source is either
reflected or absorbed by the object or scene, which is
then detected by the sensor.


Depending on the type of radiation used, a photo
converter (e.g., a phosphor screen) is typically used
to convert the energy into visible light.



Sensors that provide digital image as output, the
incoming energy is transformed into a voltage
waveform by a sensor material that is responsive to
the particular energy radiation.



The voltage waveform is then digitized to obtain
adiscrete

output.


Incoming
energy is transformed into a
voltage
by the combination of input electrical
power and sensor material.

Continuous image to be converted into digital
:
form


Sampling: digitize the coordinate values


Quantization: digitize the amplitude values


䥳獵敳⁩渠獡浰I楮朠慮搠a畡u瑩t慴楯測a牥污瑥搠瑯

.
sensors


Conventions



Origin at the top


left corner



x increases from
left to right



y increases from
top to bottom


Each element of
the matrix array is


called a pixel, for
picture element


Matrix form


bits to store the image = M x N x k

gray level =
2
k


L
-
level digital image of size
MxN



=
digital image having



a spatial resolution
MxN

pixels



a gray
-
level resolution of L levels


Spatial resolution determined by sampling



Smallest discernible detail in an image



Gray
-
level resolution determined by number
of gray scales


Smallest change in gray level


Down
-
sampling



Up
-
sampling



2
k
-
level digital image of size
NxN



How K and N affect the image quality



How many samples and gray levels are
required for a good approximation?


Quality of an image depends on number of
pixels and gray
-
level number



i.e. the more these parameters are increased,
the closer the digitized array approximates
the original image.



But: Storage & processing requirements
increase rapidly as a function of N, M, and k


Operations applied to digital images:




Zoom: up
-
sampling



Pixel duplication



Bi
-
linear interpolation



Shrink: down
-
sampling