A Standardized A Standardized Workflow for Workflow for Illumination- Illumination- Invariant Image Invariant Image Extraction Extraction Mark S. Drew Mark S. Drew Muntaseer Salahuddin Muntaseer Salahuddin Alireza Fathi Alireza Fathi Simon Fraser University, Simon Fraser University, Vancouver, Canada Vancouver, Canada {mark,msalahud,alirezaf}@cs.sfu.ca {mark,msalahud,alirezaf}@cs.sfu.ca www.cs.sfu.ca/~mark www.cs.sfu.ca/~mark
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A Standardized Workflow for Illumination-Invariant Image Extraction Mark S. Drew Muntaseer Salahuddin Alireza Fathi Simon Fraser University, Vancouver,
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A Standardized Workflow A Standardized Workflow for Illumination-Invariant for Illumination-Invariant
Image ExtractionImage Extraction
Mark S. DrewMark S. Drew
Muntaseer SalahuddinMuntaseer Salahuddin
Alireza FathiAlireza Fathi
Simon Fraser University, Vancouver, CanadaSimon Fraser University, Vancouver, Canada{mark,msalahud,alirezaf}@cs.sfu.ca{mark,msalahud,alirezaf}@cs.sfu.ca
www.cs.sfu.ca/~markwww.cs.sfu.ca/~mark
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IntroductionIntroduction
Illumination-invariant image extraction is Illumination-invariant image extraction is an interesting and open problem in vision.an interesting and open problem in vision.
illustration shows the objective:illustration shows the objective:
(the “intrinsic image”)
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Introduction (cont.)Introduction (cont.)
To obtain (b) from (a), we take the To obtain (b) from (a), we take the logarithm of band-ratio chromaticity colour logarithm of band-ratio chromaticity colour coordinates, and then project in a coordinates, and then project in a special special directiondirection [Finlayson and Hordley, 2001].[Finlayson and Hordley, 2001].
The resultant grey-scale image is The resultant grey-scale image is illumination invariant.illumination invariant.
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Introduction (cont.)Introduction (cont.)
Objective: we argue that Objective: we argue that sharpening sRGBsharpening sRGB allows us to allows us to findfind the invariant image as a generic the invariant image as a generic workflow for images, from workflow for images, from unknown camerasunknown cameras unknown actual special directionunknown actual special direction no complex algorithm using evidence in each imageno complex algorithm using evidence in each image
Works well (but not as well as knowing the Works well (but not as well as knowing the camera or using internal evidence in image!)camera or using internal evidence in image!)
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Illumination invariant is crucial step!Illumination invariant is crucial step!
Shadow RemovalShadow Removal
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Finding directionFinding direction
The direction of projection is crucial.The direction of projection is crucial.
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Finding direction…Finding direction… Could calibrate the camera to find the Could calibrate the camera to find the
invariant directioninvariant direction [Finlayson et al. (2002)]:[Finlayson et al. (2002)]:
HP912 Digital Still Camera: Log-chromaticities of 24 patches;6 patches, imaged under 9 illuminants.
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Finding direction…Finding direction… Without calibrating the camera, can use entropy of Without calibrating the camera, can use entropy of
projection to find the invariant direction projection to find the invariant direction [Finlayson et al. (2004)][Finlayson et al. (2004)]::
Cor
rect
dire
ctio
n –
smal
ler e
ntro
py
Wro
ng d
irect
ion
– hi
gher
ent
ropy
Uses internal evidence in image.
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This paper: This paper: Sharpening HelpsSharpening Helps
Argument at AIC05 Argument at AIC05 [Finlayson et al. 2005] [Finlayson et al. 2005] : : recommended that if we recommended that if we sharpen the sharpen the values in XYZ spacevalues in XYZ space, get better invariant., get better invariant.
However, going from sRGB to XYZ is However, going from sRGB to XYZ is a broadening transform: a counter-a broadening transform: a counter-intuitive approach.intuitive approach.
Therefore we propose to sharpen the Therefore we propose to sharpen the sRGB space itself.sRGB space itself.
Av. of Std. Dev. across illuminants= 6.11% not as good as calibrated version, of course! but usable.
Standardized method:Standardized method:
Standardized method:Standardized method:input chromaticity, segmented for display
output, segmented
shadow gone√
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ConclusionConclusion
The sharpening transform does a good The sharpening transform does a good enough job finding an invariant, given that enough job finding an invariant, given that it does not depend on any information it does not depend on any information specific to the camera or even the image.specific to the camera or even the image.
It can serve as a preprocessing step to It can serve as a preprocessing step to many different vision problems.many different vision problems.