Post on 15-Jan-2016
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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.