multispectral image invariant to illumination colour, strength, and shading
DESCRIPTION
Multispectral Image Invariant to Illumination Colour, Strength, and Shading. Mark S. Drew and Amin Yazdani Salekdeh School of Computing Science, Simon Fraser University, Vancouver, BC, Canada {mark/ayazdani}@cs.sfu.ca. Table of Contents. Introduction RGB Illumination Invariant - PowerPoint PPT PresentationTRANSCRIPT
Mark S. Drew and Amin Yazdani Salekdeh
School of Computing Science,Simon Fraser University,Vancouver, BC, Canada
{mark/ayazdani}@cs.sfu.ca
Multispectral Image Invariant to Illumination Colour, Strength, and
Shading
Table of ContentsIntroductionRGB Illumination InvariantMultispectral Image FormationSynthetic Multispectral ImagesMeasured Multispectral ImagesConclusion
2
IntroductionInvariant Images – RGB:
Information from one pixel, with calibrationInformation from all pixels – use entropy
New Multispectral data: Information from one pixel without
calibration, but knowledge of narrowband sensors peak wavelengths
3
RGB Illumination Invariant
4
Removing Shadows from Images, ECCV 2002Graham Finlayson, Steven Hordley, and Mark Drew
-0.5 0 0.5 1 1.5 2 2.5 3-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
log(r/g)lo
g(b
/g)
An example, with delta function sensitivities
400 450 500 550 600 650 7000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Wavelength
Re
lati
ve
Se
ns
itiv
ity
B
W R
YG
PNarrow-band
(delta-function sensitivities)
Log-opponent chromaticities for 6 surfaces under 9 lights
RGB…
Deriving the Illuminant Invariant
-0.5 0 0.5 1 1.5 2 2.5 3-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
log(r/g)
log
(b/g
)
Log-opponent chromaticities for 6 surfaces under 9 lights
This axis is invariant to illuminant colour
Rotate chromaticities
RGB…
Normalized sensitivities of a SONY DXC-930 video
camera
An example with real camera data
400 450 500 550 600 650 7000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Wavelength
Re
lativ
e S
en
siti
vity
Log-opponent chromaticities for 6
surfaces under 9 different lights
RGB…
Deriving the invariant
Log-opponent chromaticities
The invariant axis is now only approximately illuminant
invariant (but hopefully good enough)
Rotate chromaticities
RGB…
Image FormationIllumination : motivate using theoretical
assumptions, then test in practicePlanck’s Law in Wien’s approximation:
Lambertian surface S(), shading is , intensity is I
Narrowband sensors qk(), k=1..31, qk()=(-k)
Specular: colour is same as colour of light (dielectric):
9
Multispectral
To equalize confidence in 31 channels, use a geometric-mean chromaticity:
Geometric Mean Chromaticity:
with
Multispectral Image Formation …
10
{ }
Multispectral Image Formation …
11
sensor-dependent
illumination-dependent
surface-dependent
So take a log to linearize in (1/T) !
Logarithm:
12
Multispectral Image Formation …
known because, in special case of multispectral, *know* k !
Only sensor-unknown is ! ( spectral-channel gains)
klog
If we could identify at least one specularity, we could recover log k ??
Nope, no pixel is free enough of surface colour .So (without a calibration) we won’t get log k, but
instead it will be the origin in the invariant space.Note: Effect of light intensity and shading removed:
31D 30-DNow let’s remove lighting colour too: we know 31-
vector (ek – eM) (-c2/k - c2/M)
Projection to (ek – eM) removes effect of light, 1/T : 30D 29-D
13
Multispectral Image Formation …
Algorithm:
-Form 31-D chromaticity k
- Take log
- Project to (ek – eM) using projector Pe
Algorithm:
What’s different from RGB? For RGB have to get “lighting-change
direction”(ek – eM) either from (i)calibration, or (ii) internal evidence (entropy) in the
image.
For multispectral, we know (ek – eM) !
First, consider synthetic images, for understanding:
16
Camera: Kodak DSC 420
31 sensor gains qk()
Surfaces: 3 spheres, reflectances from Macbeth ColorChecker
Carry out all in 31-D, but show as camera would see it.
Synthetic Images
17
Under red light, P2800
Under blue light, P10500
shading, for light 1, for light 2
Synthetic Images
18
Spectral invariant
Original: not invariant
Measured Multispectral Images
19
Under D75 Under D48
Invt. #1 Invt. #2
Measured Multispectral Images
20
In-shadow, In-light
After invt. processing
Measured Multispectral Images
21
Measured Multispectral Images
22
Measured Multispectral Images
23
ConclusionA novel method for producing illumination
invariant, multispectral image Successful in removing effects of
Illuminant strength, colour, and shading
24
Next: removing shadows from remote-sensing data.
25Multispectral Images Invariant to Illumination Colour, Strength and Shading
Thanks!
Funding: Natural Sciences and Engineering Research Council of Canada