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Ch-1 Introduction to Image Processing

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on 5 February 2013

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Transcript of Ch-1 Introduction to Image Processing


1. Write a brief note on the application of digital image processing. (Feb-2011) (7)
2. What is digital image processing? What are the principal application areas that create interest in digital image processing? Discuss applications of image processing. (Jul- 2010) (6) 1 Introduction
2 Frequency domain processing
3 Image enhancement
4 Edge detection
5 Image restoration
6 Mathematical morphology
7 Image communication
8 Image texture analysis
9 Misc. topic such as

Book: Digital image Processing by R C Gonzalez Topics Chapter 1 : Introduction Image as a 2D data
Image representation
Gray scale and Color images
image sampling and quantization Avg: 18 marks Image Processing
710404N Introduction What is an image?
Visual representation

What is the smallest part of an image?
Pixel or Picture Elements

What are the different types of image?
B&W image, Gray scale image, Color image… Why we are using image processing?
To improve visual quality
To minimize the data required to store or transmit the digital image
To extract some information

Why we are using color image processing?
Color is a powerful descriptorEasy to identify object

# Human can differentiate thousand of colors and some 25 types of gray shades What is the difference between signal, image and video?
Signal is 1D, Image is 2D & Continues rotation of images is a video

What is processing?
Extract some information or convert into another form

What is digital?
Discrete in time & discrete in value for a 1200 x 800 color image we require
1200 x 800 = 960000 pixels = 9.6 lakhs
960000 x 3 x 8 = 23040000 bits / frame = 23.04 Mb/ frame
23.04 Mb * 25 frames/ sec = 576 Mb / sec
567 Mb * 60 sec= 34560 Mb / min
34560 Mb * 60 min = 2073600 Mb = 2073.6 Gb /hour
2073600 * 3 hour = 6220800 Mb / movie = 6220.800 Gb
6220.8 Gb / 8 Gb (DVD) = 775 Dual layer DVD
6220.8 Gb / 4 Gb (DVD) = 1555 single layer DVD
6220.8 Gb / 700 Mb (DVD) = 8982 CD
1 Movie in 1 TB hard disk How we can identify the object is Blue?

The object absorbs all light waves except of its true color and that reflected true color can be observed by human.

So we can not see in the dark. Application Remote sensing using satellite
earth resources
geographical mapping
prediction of agricultural crops
flood & fire control

Image transmission & storage for business application
broadcast television
video conference
Closed circuit camera
internet Application Medical image processing
X-rays, Gamma-rays, Ultraviolet rays
Infrared rays, Microwave rays, Radio rays
ultra sonic scanning

Radar processing
detection and reorganization of various types of targets

Automatic inspection of the industrial parts
differentiate different objects based on size, color & shape with high speed.

automation for high speed & more accuracy
more efficient compare to human
For guidance we require image processing Significance of image compression Color model, Color space, Color system

1. What do you meant by Color model? List the application of each color model. Explain any one color model in brief.(Dec-2011, Feb-2011, Jul- 2010) (7,7,3)

2. Explain HSI color model in brief and discuss the procedure for conversion from HSI to RGB color model.(Jun-2011) (5) Different color models are
CIE color space
RGB Color Spaces
Non-linear Color Spaces (HSI color space) RGB Color Model Wavelength of RGB color
Red 700 nm
Green546.1 nm
Blue 435.8 nm

The primary colors are added to produce secondary colors
Red + Green + Blue → White
Red + Green→ Yellow
Green + Blue→ Cyan
Blue + Red → Magenta Secondary color Combination of secondary color
Cyan + Magenta-->Blue
Magenta + Yellow-->Red
Yellow + Cyan-->Green
Cyan + Magenta + Yellow-->Black It provides standard notation for each color

It is one type of coordinate system and subspace where each color is represented by a single point

They are defined for hardware (monitor, color tv..) or for color computation (graphics design, animation..)

Color monitors, cameras- RGB
Color printing-CMY, CMYK
Image algorithms- HSI Based on Cartesian coordinate system

Color space is cube shape

At three corners Primary colors (RGB)

Other three corners Secondary color (CMY)

Black Is at origin

White is at corner opposite of origin

Gray values lies on the line joining black and white corner

Normalized RGB values lies on 0 to 1 Pixel Depth: The number of bits used for each pixel
For 256 level of each color RGB we require 8-bit for each color so total 24-bits are required: Pixel depth =24
Color Depth: The number of bits used for each colorFor above example Color depth is 8.
Total number of colors in 24-bit image = 2^24 = 16,777,216
The line joining white & black corner in RGB color space is known as “Intensity axis”. CMY & CMYK color space Color printer use CMY (CMYK) color space so we have to convert RGB to CMY color space.

Here RGB values are normalized to [0,1].

C+M+Y produce dull black, so one more color is required for printing which is black. So we are using CMYK model. HSI Non-linear Colour Spaces

Define wavelength of light
Define color

Relative purity of Color
Amount of white light
Pure RGB are fully saturated (No white color)
Pink is the mixing of red + white so it is less saturated then red Saturation = 1 / amount of white

Brightness or Intensity or value
Gray level

HIS model is very useful in color processing algorithm to differentiate colors. RGB HSI conversion CIE chromaticity diagram The color points inside the diagram are the mixing of spectrum colors (less saturated colors)

The line of equal energy represents the color having equal amount of RGB colors

Diagram is useful for color mixing

Straight line joining by any two color represent the various combination of color by mixing this two colors

Similarly for three color combination draw triangle using three color point and any color within the triangle can be produced using three color

So all color can not be obtained by RGB (By linear combination) The CIE XYZ colour space is one quite popular standard

It is positive value coordinate system

CIE is widely used in vision and graphics textbooks and in some applications.

Similar to 2 dimensional color system

Represent color composition as a function of x (red) and y (green)

Blue (z) can be calculated by z=1-x-y

Position of various pure spectrum colors from violet to red are on boundary of diagram (fully saturated colors) CIE chromaticity diagram Pseudocolor image processing Q: Explain the principle of Pseudocolor image processing. Jun-2011 (2)
Q: Define Full color and Pseudo color processing. Jul- 2010 (3) Pseudocolor image processing is also known as false color image processing

Practical use of pseudocolor image processing is for human visual information and interpretation of gray-scale image.

This technique is very useful because of human can differentiate thousand of color while some few gray levels.

False color implies mapping a color or gray scale image into another color image to provide a more striking color contrast to attract the attention of the viewer.

Usually the mapping is determined such that different features of the data set can be distinguished by different color. Intensity slicing Intensity (density) slicing is one of the simplest pseudocolor image processing technique.

If intensity I > Some specific value One color
If intensity I < Some specific value Second color

It is very meaningful techniques when intensity slicing is based on some physical characteristics of an image. X-ray image with cracks Size: The size of an image is determined directly from the width M (number of columns) and the height N (number of rows) of the image matrix I.
Spatial Resolution: The spatial resolution of an image specifies the spatial dimensions of the image in the real world and is given as the number of image elements per measurement; for example, dots per inch (dpi) or lines per inch (lpi) for print production, or in pixels per kilometer for satellite images.
Intensity Resolution: The intensity resolution is defined as the difference between to continuouss intensity value. image sampling & quantization
Image as 2D data
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