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This ppt's introduced Basics of computer graphics, which helps to diploma in computer engineering, DCA BCA, BE computer science student's to improve study in computer graphics.
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This ppt's introduced Basics of computer graphics, which helps to diploma in computer engineering, DCA BCA, BE computer science student's to improve study in computer graphics.
It is the basic introduction of how the images will be captured and converted form analog to digital format by using sampling and quantization process and further algorithms will be apply on the digitized image.
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for online training on
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Please feel free to contact
https://www.seoskills.in
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5.
Describe any application or technology
that can be used to represent :
-Text, image, sound, animation, video
Using a combination of moving pictures,
sound, music and words especially in
computer or entertainment.
37. Multimedia Elements
Digital Image
Main Points:
Introduction.
The importance of Image.
Image types.
Image processing & image matching .
Image representation and calculating file
size.
38. Introduction
Using of image and graphics over
centuries ago, and people used
images instead of texts.
( One image is better than 1000
words.).
Bartilean system in 1920 was the first
system that used image processing
method to transfer the newspaper from
London to New York over Atlantic ocean,
which decreased the sending time from
one week to 3 hours.
39. Image processing is used in Medicine
( X-rays , Ultra sound , endoscopy,
RMI), in Mining (Patrol , cold , …), in
space (NASA) in Military Purposes,
and in many areas(Education,
Telecommunications, Simulation).
40. Importance of image:
Since 1964 , the image processing is a
growing area and now ( in 5th Generation)
computers are using image and audio to
communicate with the user.
Image processing is used in space system(
Ringer – 7) which sent images from the Moon in
1964.
Also used in military purposes as mentioned
earlier.
Image processing is an important for any
computer area and is a rich research
41. Digital Image representation:
Digital Image is composed of number of
pixels(picture element) and it can be
represented using 2D array ( Matrix ).( N
x N array )
For monochrome images ( B / W)
( Mono TV is 512 x 512 )
image can be expressed as a
function(relation )like this :
1 0 1
0 0 1
1 1 1
42. f(x,y) = 0 ( black point).
f(x,y) = 1 ( white point).
( Ibn El-Haithem discovered that eye
can’t see in darkness ).
43. Image Types:
There are three types of image:
Binary Image.
Gray Image.
Color Image.
Binary Image : it represented as one pixel in
one bit ( e.g 100 pixel stored in 100 bits)
Gray Scale image :
.whitetoblackfromshadesmonochromaticofrangeaisGrayscale
Therefore, a grayscale image contains only shades of gray and no
color.
white)andblack(orgrayscaleassavedbecanimagesdigitalWhile
images, even color images contain grayscale information. This is
.coloritsofregardlessvalue,luminanceahaspixeleachbecause
44. Luminance can also be described as
brightness or intensity, which can be
measured on a scale from black (zero
intensity) to white (full intensity). Most
support a minimumfile formatsimage
of 8-bit grayscale, which provides 2^8
or 256 levels of luminance per pixel.
Some formats support 16-bit grayscale,
which provides 2^16 or 65,536 levels of
luminance.
45. Color Image:
thatdigital imageis a(digital) color imageA
.pixelinformation for eachcolorincludes
For visually acceptable results, it is necessary
samples(and almost sufficient) to provide three
(color channels) for each pixel, which are
interpreted as coordinates in some color space.
(Red , Green , Blue) color space isRGBThe
commonly used in computer displays.
A color image has three values per pixel and
they measure the intensity and chrominance of
light.
Thus they used 24 bit to represent each
46. Image Matching(Recognition)
Steps:
Digital image processing is the use of
computer algorithms to perform image
processing on digital images. As a subcategory
or field of digital signal processing, digital
image processing has many advantages over
analog image processing. It allows a much
wider range of algorithms to be applied to the
input data and can avoid problems such as the
build-up of noise and signal distortion during
processing.
There are 6 steps to perform image matching
processing(comparing of two images):
1. Image Scanning of capturing ( by using
47. 3- Segmentation : means separating of
important information (e.g extracting
of body from background).
4- Feature Extraction: (extracting of
features (attributes ).
5- Feature Classification(grouping of
features).
6- Image understanding(Matching)
48. Image Representation:
Depth: the depth of an image is the
number of bits used to represent
each pixel.
e.g in B/W (bitmap image ) : the depth
is calculated as :
One bit for each pixel.
4 bit: represents 16 colors(2 ^ 4 ):
used in low resolution screens.
49. 8 bit: can have 256 colors.
8 bit gray : can have 256 gray levels.
16 bit : can have 65,536 colors (hi color
in windows).
16 bit divided into ( 5 bits for Red , 6
bits for Green , 5 bit for Blue).
24 bit: can have 16,777,216 colors each
byte is used to represent the intensity of
a primary color(RGB). And each color
can have 256 different levels.
50. Resolution : How much details an
image can have , there are several
resolutions relating to image.
1- Image Resolution : is the number of
pixels in an image ( e.g 320 x 240 =
76000 pixels).
2- Display ( Monitor ) Resolution: refer
to number of dots per inch ( dpi) on a
monitor (windows usually has 96 dpi
resolution)
51. Example : A 288 x 216 image
displayed on a monitor with 96 dpi
will be :
[ 3 inch x 2.25 inch]( image size)
3- Output Resolution: refer to number
of dot per inch on a hard copy
(output device).
Many printers have 300 dpi or 600 dpi
resolution.
( the above image printed on a 300 dpi
52. Vector Graphics: instead of using pixels , objects
can be represented by their attributes such as ,
size , color , location.
This type of graphics known as vector graphic.
Vector graphic file contains graphics primitives for
example rectangle , circle , lines.
There are many languages for describing vector
graphics ( e.g VRML , SVG).
53. Calculation file size :
File size = depth x image size
[Depth (k)=2 ^ k = n , n is number of levels]
[Image Size = Area x R ^ 2]
Calculation of image size steps:
1- calculate k [ 2^ k = n , n is number
of levels)
2- calculate the total number of pixels
TNP = area x R^2 ( R, Resolution )
3- calculate the file size
FS = [TNP x K ] KB
54. Example:
A 32 level gray image with 10 dpi
resolution and 7 x 8 inch , calculate
the file size (required space in Hard
disk).
Steps:
1- cal k = 2 ^ k = n
2 ^ k = 32 , k = 5 bits.(k is depth).
2- cal TNP = area x R ^ 2
= 7 x 8 x 100 = 5600 pixels.
3- cal file size
55. H.W
Calculate the File size for the
following images:
1- A 256 color image with a resolution
of 5 dpi and an area of 100 inch.
2- a binary image with 36000 pixels.
[Hint: there is a relation between the
image resolution and the file
size][high resolution requires big size
on the disk]