BIM472 Image processing

1
BIM472 Image processing

• Color Fundamentals

2
Contents

• Color Image Processing
• Color Fundamentals
• Color Models
• RGB, CMY, CMYK, HSI
• Pseudocolor Image Processing
• Intensity Slicing
• Gray Level to Color Transformations
• MATLAB Exercises

3
Why Color?

• Color is a powerful descriptor that often
  simplifies object identification and extraction
  from a scene.
• Humans can discern thousands of color shades and
  intensities, compared to about only two dozen
  shades of gray.

4
Color Image Processing

• Full-Color Processing
• Images are acquired with a full-color sensor,
  such as a color TV camera or color scanner
• Pseudo-Color Processing
• A color is assigned to a particular range of gray
  levels

5
Color Spectrum
6
Color Fundamentals

• The colors that humans and some other animals
  perceive in an object are determined by the
  nature of the light reflected from the object.
• A body that reflects light that is balanced in
  all visible wavelengths appears white to the
  observer.
• Green objects reflect green light and absorbs
  other colors.

7
Color Fundamentals

• Achromatic Light
• Void of color
• Its only attribute is its intensity, or amount
• Gray level refers to a scalar measure of
  intensity
• Chromatic Light
• Colored light
• Spans the electomagnetic spectrum from
  approximately 400nm-700nm

8
Chromatic Light

• Radiance
• Total amount of energy that flows from the light
  source
• Measured in watts (W)
• Luminance
• Amount of energy that an observer perceives
• Measured in lumens (lm)
• Brightness
• Subjective descriptor that is practically
  impossible to measure

9
Human Vision

• Cones are the sensors in the eye responsible for
  color vision
• 6 to 7 million cones can be divided into three
  principal sensing categories Red, green, blue
• 65 are sensitive to red, 33 are sensitive to
  green and 2 are sensitive to blue
• Blue cones are the most sensitive
• Colors are seen as variable combinations of the
  primary colors red, green and blue

10
Absorption of Light by Cones
11
Secondary Colors

• Primary colors can be added to produce the
  secondary colors magenta, cyan, and yellow
• Mixing the three primaries, or a secondary with
  its primary color, in the right intensities
  produces white light

12
(No Transcript)
13
Mixtures of Pigments

• On a color TV tube, red-green-blue lights are
  emmitted from phospor dots and they are added.
• On a printed paper, red-green-blue are absorbed
  (or subtracted). Therefore primary colors for
  pigments are cyan-magenta-yellow

14
Characteristics of Colors

• Brightness
• Chromatic notion of intensity
• Hue
• Represents the dominant color perceived by an
  observer
• When we call an object red, orange, or yellow, we
  are specifying its hue
• Saturation
• Amount of white light mixed with hue (Inversely
  proportional)
• Hue and saturation taken together are called
  chromaticity.

15
Color Representation

• The amounts of red, green, and blue needed to
  form any particular color are called the
  tristimulus values and are denoted X, Y, and Z
• A color is then specified by its trichromatic
  coefficients

16
(No Transcript)
17
(No Transcript)
18
Color Models

• A color model is a specification of a coordinate
  system and a subspace within that system where
  each color is represented by a single point
• RGB
• Color monitors and video cameras
• CMY and CMYK
• Printers
• HSI
• Corresponds to how humans describe and interpret
  color

19
RGB Color Model

• Each color appears in its primary spectral
  components of red, green, and blue
• This model is based on Cartesian coordinate
  system
• If each of the red, green, and blue layers are
  represented by 8 bits, then the full-color image
  is said to be 24-bit RGB color image and
  16,777,216 different colors can be represented.

20
RGB Color Model
21
RGB Color Cube
22
(No Transcript)
23
Safe RGB Colors

• Many systems are limited to 256 colors
• Some systems cant use more than a few hundred
  colors
• The subset of colors that are likely to be
  reproduced faithfully, reasonably independently
  of viewer hardware capabilities is called the set
  of safe RGB colors, or the set of
  all-systems-safe colors

24
Safe RGB Colors
25
RGB Safe-Color Cube
26
CMY and CMYK Color Models

• Equal amounts of cyan, magenta, and yellow should
  produce black
• In practice, combining these colors produces a
  muddy looking black
• In order to produce true black, a fourth color,
  black is added and CMYK model is constructed

27
HSI Color Model

• We dont describe color of an automobile as the
  red, green, and blue components
• We describe it by its hue, saturation and
  brightness (intensity)
• HSI model is an ideal tool for developing image
  processing algorithms based on color descriptions
  that are natural and intuitive to humans

28
Relationship Between RGB and HSI
29
(No Transcript)
30
(No Transcript)
31
Converting from RGB to HSI
32
HSI Components of Color Cube
33
HSI Components of Primary and Secondary Colors
34
Manipulating HSI Components
35
Pseudocolor Image Processing

• Pseudocolor (also called false color) image
  processing consists of assigning colors to gray
  values based on a specified criterion
• The term pseudo or false color is used to
  differentiate the process of assigning colors to
  monochrome images from the processes associated
  with true color images

36
Intensity Slicing

• Gray-level image is interpreted as a 3D function
• Then the image is sliced by the planes parallel
  to the coordinate axis
• Each slice are represented by different colors

37
(No Transcript)
38
Alternative Representation
39
Example
40
(No Transcript)
41
(No Transcript)
42
Gray Level to Color Transformation
43
(No Transcript)
44
(No Transcript)
45
If Several Monochrome Images are Available
46
(No Transcript)
47
(No Transcript)
48
Using MATLAB for Image Processing
49
Color Image Representation

• An RGB color image is an MN3 array of color
  pixels, where each color pixel is a triplet
  corresponding to the red, green, and blue
  components of the image at a specified spatial
  location

50
Color Image Representation
51
Forming an RGB Color Image

• rgb_image cat(3, fR, fG, fB)
• fR rgb_image(, , 1)
• fG rgb_image(, , 2)
• fB rgb_image(, , 3)

52
Indexed Image

• An indexed image has two components A data
  matrix of integers, X, and a colormap matrix,
  map. Matrix map is m3 array of class double
  containing floating point values in the range
  0,1.
• A colormap is stored with an indexed image and is
  automatically loaded with the image when imread
  is used to load the image.
• imshow(X, map)
• image(X)
• colormap(map)

53
Indexed Image
54
RGB Values of Some Basic Colors
55
Predefined Colormaps

• colormap(copper)
• colormap(autumn)
• colormap(gray)
• imshow(x, copper)

56
(No Transcript)
57
Examples

• Generating a 100x100 yellow-red flag
• r ones(100, 100)
• b zeros(100, 100)
• g zeros(100, 100)
• g(150, 150) 1
• g(51100, 51100) 1
• f cat(3, r, g, b)
• imshow(f)
• Changing the order of r, g, b components
• f cat(3, g, b, r)
• imshow(f)

58
Examples

• Applying colormaps
• f imread('lena_gray.tif')
• imshow(f)
• imshow(f, autumn)
• imshow(f, copper)
• Applying colormaps using 'colormap' command
• imshow(f)
• colormap(copper)
• Applying a custom colormap
• map zeros(256, 3)
• map(1128, 1) 1
• map(129256, 3) 1
• imshow(f, map)