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1ST Long

Color Image Processing

1. Introduction

Color image processing is processing on colored images. 

Unlike grayscale images that only contain intensity values, color images offer a richer representation by using different color models. 

Color images processing is important for various applications such as object recognition, image enhancement, and computer vision.

2. Color Fundamentals





Color is observed based on the way objects absorb and reflect light. Different objects reflect different wavelengths of light, which our eyes interpret as color. For example:

  • A blue object reflects blue light while absorbing other colors.

  • Grey objects reflect and absorb all frequencies equally, appearing neutral.

The human eye detects colors using three types of cone cells, each sensitive to different wavelengths:

  • Red (700 nm) - 65% of cones are sensitive to this.

  • Green (546.1 nm) - 33% of cones are sensitive.

  • Blue (435.8 nm) - 2% of cones are sensitive but are the most responsive.

Hue, Saturation, and Intensity

  • Hue (H): Represents the dominant wavelength of a color, which determines the type of color (e.g., red, blue, green).

  • Saturation (S): Saturation shows how pure a color is. High saturation means the color is vivid, with no white mixed in. Low saturation is mixture of white, gray, or black that makes the color look faded or dull.

  • Intensity (I): Represents the brightness of a color, determined by the total amount of light energy.


3. Chromaticity Diagram

The chromaticity diagram represents all possible colors by plotting the proportions of red, green, and blue. where x + y + z = 1.


Diagram: Chromaticity Diagram (Insert chromaticity diagram from slide)








Explanation:

  • The diagram maps different color wavelengths.

  • White light is at the center, representing equal amounts of all wavelengths.

  • The curved boundary shows pure spectral colors, while the inside area represents mixtures.

4. Color Models

Different color models are used depending on the application. The three main models in image processing are RGB, CMY/CMYK, and HSI.

4.1 RGB Color Model

The RGB (Red, Green, Blue) model is an additive color model used in digital displays such as computer screens, televisions, and projectors. Colors are formed by combining different intensities of red, green, and blue light.

How It Works:

  • The three primary colors (Red, Green, Blue) mix to form other colors.

  • When all three colors are at full intensity, the result is white.

  • When no color is present, the result is black.

  • The mixing process is called additive color mixing.

Diagram: RGB Model (Insert RGB model diagram from slide)












4.2 CMY and CMYK Color Models

The CMY (Cyan, Magenta, Yellow) model is a subtractive color model mainly used in printing. Unlike RGB, which adds light, CMY works by subtracting light.

How It Works:

  • The primary colors are Cyan, Magenta, and Yellow.

  • These colors absorb light instead of emitting it.

  • When all three colors mix, they ideally create black, but in reality, it results in dark brown.

  • To get a true black, Black (K) is added, forming the CMYK model.

Diagram: CMY Model (Insert CMY model diagram from slide)






5. Comparison of RGB and CMY/CMYK Models

FeatureRGB ModelCMY/CMYK Model
TypeAdditiveSubtractive
Primary ColorsRed, Green, BlueCyan, Magenta, Yellow (+ Black)
Mixing ProcessAdding lightAbsorbing light
All Colors CombinedWhiteBlack
Used InScreens, digital displaysPrinting, painting

HSI (Hue, Saturation, Intensity) and HSV

HSI (Hue, Saturation, Intensity) and HSV (Hue, Saturation, Value) are two color models used to represent colors in a way that aligns more closely with human perception than the traditional RGB model.

  • HSI Model: Focuses on Hue (color type), Saturation (color purity), and Intensity (brightness level). It is useful for image processing and vision applications because intensity is separated from color information.
  • HSV Model: Uses Hue (color type), Saturation (color vividness), and Value (lightness/darkness). It is popular in graphics and digital design since the "Value" component directly affects brightness.

Applications of Color Image Processing

  • 8.1 Skin Color Detection: Used in face detection and human tracking by analyzing HSI for skin tones.
  • 8.2 Object Recognition: Helps identify objects in tasks like quality inspection and traffic sign recognition.
  • 8.3 Image Enhancement: Improves contrast, brightness, and color balance in medical imaging, satellites, and photography.

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