Nuclei of fungus cell Paramecium Caudatum
description
Transcript of Nuclei of fungus cell Paramecium Caudatum
ter Haar Romeny, FEV
Nuclei of fungus cell Paramecium Caudatum
Spatial gradient Illumination spectrum-invariant gradient
Color RGB original
Color-Scale Differential Structure
Geusebroek et al, LNCS 1852, 459-464, 1999
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The color of an object depends on
• color of the illuminating light• illumination intensity• sensor sensitivity• direction of surface normal• surface reflectance properties
Assumptions:• Scene is uniformly illuminated• light source is colored• surface has Lambertian reflectance
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What causes color ?
Lamp
objectcolor
spectral
color
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400 450 500 550 600 650 700
500 1000 1500 2000
50000
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300000
350000
400 450 500 550 600 650 700 400 450 500 550 600 650 700 400 450 500 550 600 650 700
Object reflectance function for the observed spectrum for a resp. 2500K, 6500K and 10,000K light source:
Spectrum reflected froman arbitrary object
8 hc 5E hc
kT 11
Emissionspectrum ofblack bodyradiator
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Color receptive fields
x
y
x
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Self-organization:receptive fieldsfrom Eigenpatches(12x12 pixels)
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Blakemore C, Cooper GF (1970), Development of the brain depends on the visual environment. Nature 228, 477 - 478
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Colour receptivefields fromEigenpatches
x
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GD Field et al. Nature 467, 673-677 (2010)
doi:10.1038/nature09424
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Distribution of cone cells in the fovea of an individual with normal color vision (left), and a color blind (protanopic) retina. Note that the center of the fovea holds very few blue-sensitive cones. [Wikipedia]
ter Haar Romeny, FEVGD Field et al. Nature 467, 673-677 (2010) doi:10.1038/nature09424
Full functional sampling of cone lattice by four RGC types.
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The color opponency model
Hering E, 1964. Outlines of a Theory of the Light Sense. Harvard University Press, Cambridge, Mass.
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Hering basis
0 10 20 30 40 50 60 70 80 90-0.4
-0.3
-0.2
-0.1
0
0.1
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Idea Koenderink: Gaussian derivatives of zero, first and second order in the wavelength domain
wavelength
RFsensitivity
How can weanalyse colordifferential structure?
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Taylor color model
Luminance
Blue-yellowness
Purple-greenness
300 400 500 600 700 800 90000.10.20.30.40.50.60.70.80.9
LM
S
Cone sensitivity
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Spatial color
Color scale-space starts by probing this space.
y
x
xy
s
Energy densities cannot be measured at a point, …… one probes a certain volume
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Reflectance of light
Lamp
objectcolor
spectral
color
What are invariantproperties?
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Reflectance model
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Transparent materials
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)()),,(1()()( 2 RvsneE f
The reflected spectrum is:
v = viewing directionn = surface patch normals = direction of illuminationf = Fresnel front surface reflectance coefficient in vR = body reflectance
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),())(1)(,(),( 2 xRxxexE f
Because of projection of the energy distribution on the image plane the vectors n, s and v will depend on the position at the imaging plane. So the energy at a point x is then related to:
We assume an illumination with a locally constant color:
),())(1)(()(),( 2 xRxxiexE f
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Aim: describe material changes independentof the illumination.
Rxxie
exRxxi
E
f
f
2
2
))(1)(()(
),())(1)((
),())(1)(()(),( 2 xRxxiexE f
Bothequationshavemanycommonterms
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R
xR
e
e
E
EE
),(
1
)(
11ˆ
The normalized differential
determines material changes independent of the viewpoint, surface orientation, illumination direction, illumination intensity and illumination color!
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The derivative jet to x and forms a complete family of geometric invariants:
0
ˆˆ ( ; 515 ; 55 )
n
n
EE G nm nm
These are observed properties, so we convolve with Gaussian derivatives
ˆn m
n m
E
x
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2 2ˆ ˆx yE E
Color edges can be defined as the thresholding of the spatial gradient (color-invariant equivalent of Lw):
2 2ˆ ˆx yE E
Color invariants
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Spatial color model and tracing color edges in microscopy
Influence of illumination color temperature on edge strength, scale is 3.0 px.
Skin tissue section illuminated by a halogen bulb at 4000 K (top) and 2600 K (bottom) color temperature.
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1
ee
x. ex, y, eshortnotatione exzx, y, exx, y, ezx, y,
e2
x,. ex, y, eshortnotation1
e3e2 exzzx, y, 2 e exzx, y, ezx, y, exx, y, 2 ezx, y, 2 e ezzx, y,
Color-invariant multi-scale structural operators
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Simplifyx2 y2 x, 2 y, 2;shortnotation 1
ex, y, 6 ex, y, 2ex, y, exzx, y, exx, y, ezx, y, 2
ex, y, 2ex, y, eyzx, y, eyx, y, ezx, y, 2 ex, y, 2 exzzx, y, 2 exx, y, ezx, y, 2 ex, y, 2 exzx, y, ezx, y, exx, y, ezzx, y, 2 ex, y, 2 eyzzx, y, 2 eyx, y, ezx, y, 2 ex, y, 2 eyzx, y, ezx, y, eyx, y, ezzx, y, 2
Total edge strength
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[im,] 1e
e
color invariant 1e
e
[im,]
first wavelength derivative of
[im,] 22 second wavelength derivative of
g[im,] x2 y2 yellow-blue edges
g [im,] 2x2 2
y
2 red-green edges
g[im,] x2 y2
2x2 2
y
2 total color edge strength
Somecolor
differentialinvariants
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Feulgen stain,red-green edges
Paramecium caudatum, Feulgen and Fast green stain
Color canny, red-green normalized edges, scale 3
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Hematoxylin eosin stain
Pituitary gland, sheep, adenohypophysis 40x
Cell: E<0, E > 0, scale 1.0
Nuclei: E <0, E > 0, E +E < 0, scale 3.0
additional constraint added to refine selection
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Safranin O stain
E > 0, E > 0, scale sigma 1.0
Safranin O stain for proteoglycans (mouse knee
joint)
Courtesy of Koen Gijbels and Paul Stoppie
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Oil red O stain
Oil red O stain of fat emboli in
lung
E > 0, E > 0, scale 1.5
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PAS stain
Lww > 0, Lvv Lww-Lvw2 > 0, E -E > 0, scale sigma 2.0
P.A.S. stain for carbohydrates (goblet cells, gut)
carbohydrates stain magenta - elliptic patches
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Blood smear
Blood smear, Giemsa stain, 100x, JPEG
compression
RBC: E > 0, E +E > 0, scale 0.5
Leucocytes: E < 0, scale 12
Leucocyte nuclei: E < 0, E > 0, scale
3
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Blue-yellow edges
Note the complete absence of detection of black-white edges.
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Color edges can also be defined as the zero-crossings of the second order derivative in the spatial gradient direction (color-invariant equivalent of Lww):
0ˆˆ
ˆˆˆˆˆ2ˆˆˆ
22
22
yx
xxxxyxyyyyww
EE
EEEEEEEE
Second order color invariants
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Color invariant edge detection
Luminance gradientedge detection
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Conclusions
• Color ‘scale-space’ compatible with classical luminance
scale-space
• The model enables the design of practical image analysis
‘color reasoning’ solutions, e.g. invariance for illumination
• The color-scale invariant differential operators are building
blocks for a differential geometry on color images