Color spaces

CIE - RGB space.

HSV - space.

CIE - XYZ space.

L*A*B* - space.

YUV, YIQ

CMY, CMYK

CIE-XYZ Gamut

•Only the area between R,G,B can be reproduced by R,G,B primaries.

Considerations in determining the R,B,G primitives

•Producing a wide range of colors •Dynamic range considerations: don’t use colors that you don’t need.•“System” considerations: Colors that are easy to produce by color monitors.

The CMY(K) model

cian

pain

t

•L*ab color space normalizes the color space such that Euclidian distances will fit the perceptual ones (using JND)

•For example: Human vision has a nonlinear perceptual response to brightness.

•In general, I needed for just noticeable difference (JND) over background I was found to satisfy: I /I = const •Weber’s low: lightness perception is roughly logarithmic.

•This is comparable to the L* component of the perceptual L*ab color model:

Perceptual Models

Color segmentationFirst try Segment the image according to its color histogram- Find “Clusters” of colors in the RGB space (or make a color quantization)

Problem: In the RGB space, there is information which is not relevance to the chromaticity : lightening, texture and shadows. We need to eliminate the intensity !

Color segmentation (cont’)Solution: Convert the RGB image to another image space containing an intensity component- omit this component from the segmentation process.

Examples: YIQ: Omit Y HSV (Hue, Saturation, Value): Omit V

Color segmentation using the HSV model

Original image

Skin ColorTask: We would like to detect faces according to their color histograms

Skin Color: The chromaticity of skin is very restricted (mainly the “Hue” component )*

[*Skin Color is due to the amount of the pigment melanin]

Skin Color (cont’)

Hue: Saturation: Value:

White balancingProblem: The color image is effected by the color of the light. Example: Outdoor scenes are “more blue” than indoor scenes.

Possible solution: (far from perfect)Perform white balancing: find “white” regions, and change the color map such that these regions will become real white. Sometime, color skin can also be used for this purpose.

Simplified Physical Model:

• An image is a function of many parameters:

Image

Geometry Surface albedoLighting Viewing

L V

y)(x,n

y)(x,ρ

Simplified Physical Model:

• It is common to divide it into Lambertian components and specular ones.

• Lambertian: The light is returned to all direction.

• Specular: The light is returned in approx’ one direction.

• Most objects are mainly Lambertian, but have a small Specular component.

)cos()()()( iL

)(cos)()( 0 sn

sL

Simplified Physical Model:

• For Lambertian objects, the specular component is zero therefore we have a linear function.

• Pixels belonging to a region with homogenous color should lie upon a line throw the origin in the RGB histogram.

• With the Specular component, these pixels will lie on a plane (but most of the pixels will still lie on the original

line)

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The T-Shape model.• The T shape model

introduced by Klinker et al. is widely used to model specularity.

• The model assumes a large n in the previous equation => for each pixel there is only one dominant component

The color line model An ongoing work of Ido Omer and Mike Werman.

“Real Histogram” properties.

• The lines best describing the color clusters don’t intersect the origin.

Cut Off:

• One of the possible causes for the inaccuracy of the linear model is the “cut off “ phenomena in image sensors.

Looking at the histogram:

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Looking at the histogram:

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Looking at the histogram:

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Looking at the histogram:

Comparing color segmentation using different color models.

Original

HSV

Lab

Color Lines

Color segmentation with “color lines”

• slice the histogram perpendicularly to the origin.

• search for local maxima.

• combine these maxima to color lines

• Since we look only at the histogram, we are not effected by local image properties like texture.

• The number of colors in the original image > 80,000, yet it has been described using ~40 lines.

• Conclusion: The color histograms of natural images are very sparse.

Color segmentation with “color lines”

Other distortions…

Be aware: Most cameras apply various color enhancements that distorts the linear color model.

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Talking about mosaicing, the “opposite” problem also exists.

• Most digital cameras use filter arrays to sample red, green, and blue according to the Bayer pattern or similar ones.

• At each pixel only one color sample is taken, and the values of the other colors must be interpolated from neighboring samples.

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Demosaicing.

• Many demosaicing techniques refer to the green channel as the “detail channel “ and to the red/blue channels as chrominance channels.

• These techniques start by interpolating green values and then interpolate red/blue values according to the green one.