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Monday, 31 October 2016

Digital Image Processing - Color Image processing

Color Image processing

The use of color in image processing is motivated by two principal factors. First, color is a powerful descriptor that often simplifies object identification and extraction from a scene. Second, human can discern thousands of color shades and intensities, compared to about only two dozen shades of grat. This factor is particularly important in manual image analysis.

In the first category, the image in question typically are acquired with full color sensor, such as color TV camera or color scanner. In the second category, the problem is one of assigning a color to particular monochrome intensity or range of intensities.

Full color image processing techniques are now used in a broad range of applications, including publishing, visualization, and the internet.

Color images processing is divided into two major areas: - full color image processing and pseudocolor image processing.
Three basic quantities are used to describe the quality of a chromatic light source is:
1.   Radiance
2.   Luminance
3.   Brightness
Radiance is the total amount of energy that flows from the light source, and it is usually measured in watt (W).
 Luminance is measured in terms of lumens (lm), gives the measure of the amount of energy an observer perceives from a light source.
Brightness is a subjective descriptor that is practically impossible to measure.
Primary colors: - Red (R), Green (G) and Blue (B).
Secondary colors: - Magenta (red + blue), Cyan (green + blue), Yellow (red + green).
The characteristics generally used to distinguish one color from another are brightness, hue and saturation. Hue and saturation taken together are called chromaticity. And therefore a color may be characterized by its brightness and chromaticity.
Color Models: - Basically, the colors that humans and some other animals perceive in an object are determined by the nature of the light reflected from the object. Characterization of light is central to the science of color. If the light is achromatic (void of color), its only attribute is its intensity, or amount. Achromatic light is what viewers see on a black and white television set.
 The purpose of color model is facilitate the specification of colors in some standard.. Most color model used today are oriented either towards hardware or toward applications where color manipulation is a goal.
·        RGB (red, green, blue) Model
·        CMY (cyan, magenta, yellow) Model
·        CMYK (cyan, magenta, yellow, black) Model
·        HSI (hue, saturation, intensity) Model

RGB Model: - In RGB model, each color appears in its primary spectral components of red, green and blue. This model is based on Cartesian coordinate system. Image represented in RGB color model consist of three components images.

The number of bits used to represent each pixel in RGB space is called the pixel depth. The total number of colors in 24-bit RGB image is 2^24 = 14,777,216.

The term full color image is used often to denote 24-bit RGB color image.
The CMY Color Model: - Cyan, magenta and yellow are the secondary color of light or alternatively primary colors of pigments. For example, when a surface is coated with cyan pigment is illuminated with white light, no red light is reflected from surface. This is cyan reflects red light from reflected white light. Which itself is composed of equal amount of red, green and blue light.
                                                                  

The HIS Model: - Createing colos in RGB and CMY models and changing from one model to another is strainghtforward process. These color systems are ideally suited for hardware systems. When human views a object, we describe it by its hue and saturations.

(Figure: Conceptual relationship between RGB and HSI models)

The key point to keep in mind regarding the cube arrangment and its corresponding HIS color space is that HSI space is represented by a vertical intensity axis and locus of color point that lies on plan perpendicular to this axis.

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