what's the reason for converting RGB into HSI color space in...

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Answer by Eddy Wehbe
Submitted on 5/17/2005
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In machine vision, colors of the pixels are composed from Red, Green, and Blue, each measured in 8 bits (2 hexadecimal bits). The complete representation of a color would be of 6 hexadecimal bits. The problem is that a family of colors (like the orange and the combinations close to it) cannot be easily known from that 6 bits representation. Moreover, the changes in brightness will cause enormous changes in the RGB representation of a certain color. Therefore, in machine vision, the most used color representation is the HSI color space, which consists of Hue angle, color Saturation, and Intensity. To be independent of the intensity variance, we use the HS space. This will also help in making the processing and the computing faster.

Answer by saleem
Submitted on 5/11/2006
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You will notice that RGB colour space is used for colour representation on monitors, where (traditionally) three colour guns (Red, Green and Blue) are used to "bombard" the phosphorus pixels. Variation in intensities of RGB guns produces all the colours possible to be shown on a monitor. Likewise CMYK standard is meant for printing devices. HSI colour space is more near to how humans perceive the colours. In image processing, using RGB space, we can tell what is the value of R G and B not how much R is there in mixture of a colour, e.g. (255, 255, 255) means colour of the pixel is pure white and (0, 0, 0) means colour of the pixel is pure black. When we want to detect magenta (with its variations) it is hard to do so. In HSI, Hue, Saturation and Intensity are better used to detect the colour. Say if we want to deal in the brightness (which is more subjective) it is in "I". Moreover, 85% of the noise tends to be in "I" component. So it is better to process "I" for noise-removal rather than the three R = G = B components. Hue tells us purity of colour while Saturation tells us brightness + admixture of colour with other ones. That's why color detection algos operate on H + S components and the amount of "tolerance" one wants to have in colour detection.

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Question by latha
Submitted on 2/23/2004
Related FAQ: colorspace-faq -- FAQ about Color and Gamma
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what's the reason for converting RGB into HSI color space in human face detection in color images

Answer by calimero100582
Submitted on 8/9/2004
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There is many colorspace, the reason why we use HSI colorspace is because it is easier to extract each color from H value. 0 for red, 120 for green, 240 for blue. Using HSI, you so can be able to completely ignore light and saturation.

Answer by Phil
Submitted on 9/7/2005
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Please write me back and tell me the reference you have for converting RGB into HSI space for face detection. It will help prevent someone from patenting that. - philip.goetz@uspto.gov

Answer by Alan
Submitted on 10/12/2005
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It simplifies the segmentation of the image into skin and not skin. Most human skin - regardless of race - has a common hue value, and so is a natural choice for detecting skin. HSI also has the advantage of partially countering the effect of different lighting levels by splitting off the lightness value (intensity).

Answer by shr
Submitted on 7/4/2006
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mr X is an image processor. he wants to find yellow pixels in a bitmap , the computer scans ... red=123 green=139 blue=142 how can the computer know is it yellow or not ? but with hsi colors we can easily know.

Answer by nerosky
Submitted on 4/24/2007
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HSI color space is more precise in describing color. For example when we describe the color of face or other object we never say that how red, green or blue the face will be. Instead we describe the face in term of the brightness, the color, and also how dark or light the color will be. This is exactly what HSI color space provide you where H stands for hue will define the color, S stands for saturation will define how strong that particular color will be and I the intensities which specified the brightness.

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