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بسمه‌تعالي. Digital Image Processing. C o l o r Image Processing (Chapter 6). H.R. Pourreza. Preview. Motive - Color is a powerful descriptor that often simplifies object identification and extraction from a scene.

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digital image processing

بسمه‌تعالي

Digital Image Processing

Color Image Processing(Chapter 6)

H.R. Pourreza

H.R. Pourreza

preview
Preview
  • Motive

- Color is a powerful descriptor that often simplifies object identification and extraction from a scene.

- Human can discern thousands of color shades and intensities, compared to about only two dozen shades of gray.

H.R. Pourreza

preview1
Preview

H.R. Pourreza

preview2
Preview

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preview3
Preview
  • Color image processing is divide into two major area:
    • Full-Color Processing
    • Pseudo-Color Processing

H.R. Pourreza

color fundamentals
Color Fundamentals

The experiment of Sir Isaac Newton, in 1666.

H.R. Pourreza

slide8

Color Fundamentals (con’t)c

  • Basic quantities to describe the quality of light source:
    • Radiance: Total amount of energy that flows from the light source (in W).
    • Luminance: A measure of the amount of energy an observer perceives from the light source (in lm)
    • Brightness: A subjective descriptor that embodies the achromatic notion of intensity and is practical impossible to measure.

H.R. Pourreza

color fundamentals con t
Color Fundamentals (con’t)

Standard wavelength values for the primary colors

H.R. Pourreza

color fundamentals con t2
Color Fundamentals (con’t)
  • The characteristics generally used to distinguish one color from another are Brightness,Hue, and Saturation.
    • Hue: Represents dominant color as perceive by an observer.
    • Saturation: Relative purity or the amount of white light mixed with a hue
  • Hue and saturation taken together are called Chromaticity, and therefore, a color may be characterized by its Brightness and Chromaticity.

H.R. Pourreza

color fundamentals con t3
Color Fundamentals (con’t)
  • Tri-stimulus values: The amount of Red, Green and Blue needed to form any particular color

Denoted by: X, Y and Z

  • Tri-chromatic coefficient:

H.R. Pourreza

color fundamentals con t4
Color Fundamentals (con’t)

Chromaticity Diagram

Green Point =

62% green,

25% red,

13% blue.

H.R. Pourreza

color fundamentals con t5
Color Fundamentals (con’t)

Color Gamut produced by RGB monitors

Color Gamut produced by high quality color printing device

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color models
Color Models
  • The purpose of a color model (also called color space or color system) is to facilitate the specification of colors in some standard, generally accept way.
  • RGB(red,green,blue) : monitor, video camera.
  • CMY(cyan,magenta,yellow),CMYK (CMY, black) model for color printing.
  • and HSI model,which corresponds closely with the way humans describe and interpret color.

H.R. Pourreza

the r g b color models con t2
The RGB Color Models (con’t)

Safe RGB Colors (Safe Web colors)

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the c m y and c m y k color models
The CMY and CMYK Color Models
  • Cyan, Magenta and Yellow are the secondary colors of light
  • Most devices that deposit colored pigments on paper, such as color printers and copiers, require CMY data input.

H.R. Pourreza

slide25

The HSI Color Models

  • Converting colors from RGB to HSI

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The HSI Color Models

  • Converting colors from HIS to RGB
    • RG sector :

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slide27

The HSI Color Models

  • Converting colors from HIS to RGB
    • GB sector :

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slide28

The HSI Color Models

  • Converting colors from HIS to RGB
    • BR sector :

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The HSI Color Models

RGB

H

H

S

S

I

RGB

I

H.R. Pourreza

pseudocolor image processing
Pseudocolor Image Processing
  • Pseudocolor (also called false color) image processing consists of assigning colors to gray values based on a specified criterion.
  • The principal use of pseudocolor is for human visualization and interpretation of gray-scale events in an image or sequence of images.

H.R. Pourreza

intensity slicing
Intensity Slicing

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slide42

Basic of Full Color Image Processing

Let c represent an arbitrary vector in RGB color space

For an image of size M*N,

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slide44

Basic of Full-Color Image Processing

  • Major categories of full-color Image processing:
    • Per-color-component processing
    • Vector-based processing

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slide45

Basic of Full-Color Image ProcessingColor Transformation

Processing the components of a color image within the context of a single color model.

Color components of g

Color components of f

Color mapping functions

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slide46

Full-Color Image ProcessingColor Transformation

CMYK

  • Some difficulty in interpreting the HUE:
    • Discontinuity where 0 and 360º meet.
    • Hue is undefined for a saturation 0

RGB

HSI

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slide50

Full-Color Image ProcessingColor Transformation: Color Slicing

Motive: Highlighting a specific range of colors in an image

  • Basic Idea:
  • Display the color of interest so that they stand out from background
  • Use the region defined by the colors as a mask for further processing

H.R. Pourreza

slide51

Full-Color Image ProcessingColor Transformation: Color Slicing

1. Colors of interest are enclosed by cube (or hypercube for n>3)

2. Colors of interest are enclosed by Sphere

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slide53

Full-Color Image ProcessingColor Transformation: Tone and Color Correction

  • The tonal rang of an image, also called its key-type, refers to its general distribution of color intensities.
  • High-key images: Most of the information is concentrated at high intensities.
  • Low-key images: Most of the information is concentrated at low intensities.

H.R. Pourreza

slide54

Full-Color Image ProcessingColor Transformation: Tonal Correction

Middle-key Image

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Full-Color Image ProcessingColor Transformation: Tonal Correction

High-key Image

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Full-Color Image ProcessingColor Transformation: Tonal Correction

Low-key Image

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slide57

Full-Color Image ProcessingColor Transformation: Color Correction

The proportion of any color can be increased by decreasing the amount of the opposite (or complementary) color in the image or by raising the proportion of the two immediately adjacent colors or decreasing the percentage of the two colors adjacent to the complement.

Magenta 

Removing Red and Blue

Adding Green

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slide59

Full-Color Image ProcessingColor Transformation: Histogram Processing

Histogram Equalizing the Intensity

Saturation Adjustment

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Full-Color Image ProcessingColor Image Smoothing

Hue

Saturation

Intensity

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slide63

Full-Color Image ProcessingColor Image Smoothing

Difference

Averaging R,G and B

Averaging Intensity

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Full-Color Image ProcessingColor Image Sharpening

The Laplacian of Vector c :

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slide65

Full-Color Image ProcessingColor Image Sharpening

Difference

Sharpening R,G and B

Sharpening Intensity

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slide66

Full-Color Image ProcessingColor Segmentation

  • Segmentation is a process that partitions an image into regions
  • Segmentation in HIS Color Space
  • Segmentation in RGB Vector Space
  • Color Edge Detection

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slide68

Full-Color Image ProcessingColor Segmentation: in RGB Vector Space

z is similar to a if the distance between them is less than a specified threshold.

Euclidian Distance:

Generalized form:

H.R. Pourreza