Digital image processing

1. Color image processing

2. Two principal factors
 The use of color in image processing is motivated by two
principal factors
1.color is a powerful descriptor that often simplifies object
identification and extraction from a scene.
2.Humans can discern thousands of color shades and
intensities compared to about only two dozen shades of gray.

3. Two major areas
 Color image processing is divided into two major areas
1. Full color and
2. Pseudocolor processing
 In Full-color processing the images are acquired with a full-color sensor ,such
as a color TV camera or color scanner.
 In Pseudo-color processing ,the problem is one of assigning a color to a
particular monochrome intensity or range of intensities.

4. Color fundamentals
 Sir Issac Newton discovered that when a beam of sunlight passes through a
glass prism the emerging beam of light is not white but consists instead of
continuous spectrum of colors ranging from violet at one end to red at the
other.
 Color spectrum may be divided into six broad regions as shown in fig.
 When viewed in full-color no color in the spectrum ends abruptly but rather
each color blends smoothly into the next.
 Visible light is composed of narrow band of frequencies.

5.  The colors that humans perceive in an object are determined by the nature
of light reflected from the object.
 A body that reflects light that is balanced in all visible wavelengths appears
white to the observer.
 A body that favors reflectance in a limited range of visible spectrum
exhibits some shades of color
 Ex. green objects reflect light with wavelengths primarily in 500 to 570nm
range while absorbing most of energy at other wavelengths.
 Characterization of light is central to the science of color.
 If light is achromatic, its only attribute is its intensity or amount.
 Achromatic light is what viewers see on a black and white television set.
 Chromatic light spans the electromagnetic spectrum from approximately
400 to 700nm.

6.  Three basic quantities use to describe quality of chromatic light source are
1.Radiance 2.Luminance 3.Brightness
 Radiance is the total amount of energy that flows from the light source and is
measured in watts(w).
 Luminance is the amount of energy an observer perceives from a light source
and is measured in lumens(lm).
 Brightness is the subjective descriptor that is practically impossible to measure
and it is one of the key factors in describing color sensation.
 Cones are the sensors in the eye responsible for color vision.
 6 to 7 millions cones in human eye can be divided into three principal sensing
categories such as red, green &blue.
 Approximately 65% of all cones are sensitive to red light.
 33% are sensitive to green light and
 2% are sensitive to blue.

7. Primary colors
 Due to the absorption characteristics of the human eye, colors are seen as
variable combinations of the so called primary colors red(R) ,green(G)
,blue(B) .
 Use of the word primary has been misinterpreted to mean that three
standard primaries when mixed in various intensity proportions can
produce all visible colors.

8. Secondary colors
 Primary colors can be added to produce the secondary colors of light.
 Magenta(red plus blue)
 Cyan(green plus blue)
 Yellow(red plus green)
 Mixing three primary colors or a secondary with its opposite primary color in
the right intensities produce white light .

9.  Primary colors of pigments is defined as one that
subtracts or absorbs a primary color of light and reflects or
transmits the other two.
 Therefore primary colors of pigments are magenta, cyan,
and yellow.
 The secondary colors are red, green and blue.
 A proper combination of three pigment primaries or a
secondary with its opposite primary produces black.

10.  The characteristics generally used to distinguish one color from
another are
i. Brightness
ii. Hue
iii. Saturation
 Brightness embodies the chromatic notion of intensity.
 Hue is an attribute associated with the dominant wavelength in
a mixture of light waves.
 Saturation refers to the relative purity or the amount of white
light mixed with a hue.
 Pure spectrum colors are fully saturated.

11.  Hue and saturation together are called chromaticity.
 Thus a color may be characterized by its brightness and
chromaticity.
 The amounts of red, green and blue needed to form any particular
color are called the tristimulus values and are denoted as x, y and
z respectively.
 A color is then specified by its trichromatic coefficients, defined
as

X=x/x+y+z y=y/x+y+z z=z/x+y+z
x+y+z=1

12. Chromaticity diagram
 Another approach for specifying colors is to use the chromaticity diagram, which
shows the color composition as a function of x(red) and y(green).for any value of x
and y the corresponding value of z(blue) is obtained from the equation.
 The positions of various spectrum colors-from violet at 380nm to red at 780nm are
indicated around the boundary of tongue shaped chromaticity diagram. these are
pure colors shown in the spectrum.
 The point of equal energy corresponds to equal fractions of three primary colors, it
represents the standard for white light.
 as a point leaves the boundary and approaches the point of equal energy more white
light is added to the color and it becomes less saturated.
 The saturation at the point of equal energy is zero.
 Chromaticity diagram is useful for color mixing
x+y+z=1

14. Color models
 The purpose of color model is to facilitate the specification of
colors in some standard, generally accepted way.
 Color model is a specification of a coordinate system and a
subspace within that system where each color is represented by
a single point.
 Most color models are oriented either toward hardware or
toward applications where color manipulations is a goal.
 The hardware- oriented models most commonly used in practice
are
 RGB model for color monitors and color video cameras.
 CMY,CMYK models for color printing and
 HSI model which corresponds closely with the way humans
describe and interpret the color.

15. RGB COLOR MODEL
 In this model each color appears in its primary spectral components of red,
green and blue.
 This model is based on cartesian coordinate system
 The color subspace of interest is the cube.
 All values of R,G and B are assumed to be in the range [0,1].
 Images represented in the RGB color model consist of three component
images, one for each primary color. when fed into an RGB monitor these
three images combine on the phosphor screen to produce a composite color
image.
 Each RGB color pixel is said to have a depth of 24 bits.
 The term full-color image is used often to denote a 24-bit RGB image.

17. CMY and CMYK color models
 When a surface coated with cyan pigment is illuminated with
white light, no red light is reflected from the surface.
 i.e. cyan subtracts red light from the reflected white light.
 Most devices that deposit colored pigments on paper, such as
color printers and copiers, require CMY data input or perform an
RGB to CMY conversion internally.
 This conversion is performed using simple operation
 Light reflected from the surface coated with pure cyan does not
contain red.

18. HSI color model
 Unfortunately the RGB,CMY and other similar color models are not well
suited for describing colors in terms that are practical for human
interpretation.
 When humans view a color object we describe it by its hue, saturation and
intensity.
 Hue is a color attribute that describes the pure color.
 Saturation gives the measure of degree to which a pure color is diluted by
white light.
 Brightness is a subjective descriptor.
 This model decouples the intensity component from the color carrying
information in a color image.
 HSI model is an ideal tool for developing image processing algorithms based
on color descriptions that are natural to humans.
 Thus we can summarize that RGB is ideal for image color generation but its
use for color description is much more limited.

21. Converting Colors from RGB to HSI

22. HSI component images
R,G,B Hue
saturation
intensity

23. Converting Colors from HIS to RGB

24. Converting Colors from HIS to RGB

25. Converting Colors from HIS to RGB

26. This is also called false color image processing
The Process of gray level to color transformations is known as pseudo color image
processing.
A Pseudo color image is derived from a gray scale image by mapping each pixel
value to a color according to the function.
Pseudo color Image Processing

27. There are two techniques for Pseudo color image processing.
1. Intensity Slicing ( sometimes called density slicing)
2. Intensity to color transformation
Pseudo color Image Processing Contd.,

28. Intensity slicing
 3-D view of intensity image
Image plane
Color 1
Color 2

29. Intensity slicing (cont.)
 Alternative representation of intensity
slicing

30. Application 1
8 color regions
Radiation test pattern
* See the gradual gray-level changes

31. Application 2
X-ray image of a weld

32. Gray Level to Color Transformations

33. Combine several monochrome images

34. R G
B
Near
Infrared
(sensitive
to biomass)
R+G+B near-infrared+G+B
Washington D.C.