color image processing - sharif university of...
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E. Fatemizadeh, Sharif University of Technology, 2011
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Digital Image Processing
Color Image Processing
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• Color Image Processing:
– Full-Color Processing:
• Color is acquired with a full-color sensor
– Pseudo-Color Processing:
• Assigning colors to monochrome images
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Color Image Processing
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• Color Importance:
– Color is an excellent Descriptor:
• Suitable for object Identification and Extraction
– Discrimination:
• Humans can distinguish thousands of color shades and intensities But few shades of gray levels.
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Color Image Processing
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• Color and Light:
– Light is fundamental of color vision • Achromatic light (BW Television): Intensity!
• Chromatic light (Color TV): Radiance, luminance, and Brightness
– Light is an Electromagnetic waves: • If wavelength (pure) is between ≈400nm and ≈700nm, the wave is
detectable by human eye and called a monochromatic (Not gray level!) light.
– λ<400nm: Ultraviolet
– λ >700nm: Infrared
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Digital Image Processing
Color Image Processing
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• What is Color:
– An Attribute of objects.
– Color Depends on:
• Spectral characterizations of light source (sun!) which illuminates the objects, PSD (Power Spectral Density)
• Spectral characteristics of objects (Reflectance)
• Spectral characteristics of sensors of imaging systems (ex. Eye)
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Color Image Processing
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• Color Fundamentals:
– Physiopsychological phenomenon:
• How human brain perceive and interpret color? – Not yet fully understood
– Physical Phenomenon:
• Physical Nature of color is clear.
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Color Image Processing
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• Newton Experiments (Spectral Analyzer)
– Emerging light is not longer white but a
continuous spectrum of color from violet to red
– Six broad region: violet, blue, green, yellow, orange, and red
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Color Image Processing
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• Chromatic Light:
– Span the electromagnetic spectrum from 400 to 700 nm
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Color Image Processing
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• Chromatic Light:
– Radiance:
• Total amount of energy that flows from light source (W)
– Luminance:
• The amount of energy that an observer perceives from light source. (IR case: R may be high while L is zero )
– Brightness:
• Subjective description, practically impossible to measure.
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Color Image Processing
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• Radiance for Black-Body radiator (Plank’s law)
• For other sources similar curves exist.
1
5 2 0
( ) ( ) , Watts
exp 1
CC R C d
C
T
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Color Image Processing
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• Luminance of objects:
Illumination source Object
reflection
C(λ)
R(λ)
S(λ)
Imaging Device
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Color Image Processing
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• Luminance of objects:
visible region
L= ( ) , LumensC R S d
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Digital Image Processing
Color Image Processing
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• Brightness of objects:
– Subject dependent
– Human perception of brightness is related to c()
– The contributions that c(1) and c(2) make to human
perception of brightness are in general quite different for
1≠ 2 even though c(1) may be the same as c(2).
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Color Image Processing
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• The relative luminous efficiency of a
monochromatic light is defined as c(r) / c (t)
when c(r) and c’(t) are matched in brightness.
– c(r) is the reference monochromatic light.
– r is 555 nm (yellow-green light) at which a typical
observer has maximum brightness sensitivity.
– c’(t) is a test monochromatic light.
• For this reference, the relative luminous efficiency
is always less than or equal to 1.
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Color Image Processing
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• The relative luminous efficiency as a function of
is called the relative luminous efficiency function
and is denoted by v().
• Two monochromatic lights with c1(1) and c2(2)
appear equally bright to an observer when
– c1(1)v(1) = c2(2)v(2).
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Color Image Processing
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• Eye as light sensor:
– (6-7) millions cons cell categorized in 3 group sensors: • Red (67%)
• Green (33%)
• Blue (2%)
– Blue is most sensitive
– Standard Definition of R-G-B (CIE): • Red: 700nm
• Green: 546.1 nm
• Blue: 435.8nm
– No single color may be called R,G, or B.
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Color Image Processing
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• Relative Absorption of R/G/B cones:
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Color Image Processing
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• Absorption characteristic of eye and Primary colors:
– Curves are experimental
– Colors are seen as variable combination of primary colors (R-G-B)
– With three specific primary colors (fixed wavelength) it is NOT possible to generate all spectrum colors.
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Color Image Processing
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• Secondary Colors:
– Addition of primary colors:
• Magenta = Red + Blue
• Cyan = Green + Blue
• Yellow = Red + Green
– Mixing three primary and secondary with its opposite primary produce white color.
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Color Image Processing
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• Primary Colors of light and primary colors of pigments (colorant):
– Primary color of pigments:
• Subtract or absorbs a primary color of light and reflect the other two!
– Primary colors of light are: R-G-B
– Primary color of pigments are: C-M-Y.
– Secondary color of light are: C-M-Y.
– Secondary color of pigments are: R-G-B
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• Primary Colors of light and primary colors of pigments (colorant):
– Problem is application dependent:
• R-G-B: Primary for Color TV
• C-M-Y: Primary for Color Printer.
Color of light: R G B
Color of pigments: absorb R absorb G absorb B Cyan Magenta Yellow
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Color Image Processing
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• Color TV:
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Color Image Processing
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Light
Pigments
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Color Image Processing
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• Characteristics of color
– To distinguish a color from another
– Hue, Saturation, and Brightness
• Hue: Dominant color (wavelength) perceived by an observer. (Red, Orange, Yellow, ant etc.)
• Saturation: relative purity of color or the amount of white light mixed with a hue.
– Pure colors (δ(λ-λ0)) are fully saturated.
– Pink is less saturated.
• Brightness: chromatic notion of intensity.
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Color Image Processing
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• Characteristics of color:
– Chromaticity:
• Hue+Saturation.
– Tri-stimulus:
• The amounts of R/G/B needed to form any particular color (X,Y, and Z)
BGR
Rr
BGR
Gg
BGR
Bb
1r g b r, g, b wavelength of light for that color
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Color Image Processing
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• Chromaticity Diagram:
– Color composition as a function of red (x-axis) and green (y-axis).
– Blue = 1-Red-Green
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Color Image Processing
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• Real and Imaginary Color – Negative Color
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Color Image Processing
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1. Boundary are full saturated (Pure Color)
1b r g
2. Mixed Color
3. CIR White (1/3,1/3,1/3) -zero saturation
5. Color Mixing With various Value of 2 color
4. All shade Of Pure color
6. Color Mixing With various Value of 3 color
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Color Image Processing
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• Chromaticity Diagram:
– Color composition as a function of red (x-axis) and green (y-axis).
– Blue = 1-Red-Green
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Color Image Processing
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• Chromaticity Diagram:
– Not all color in chromaticity diagram are enclosed by a triangle!
• With three single primary color we can NOT have all possible color!
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Color Image Processing
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Chromaticity Diagram
Monitor colors
Printers Inks
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Color Image Processing
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• Color Model (Color Space, Color System)
– Specify colors in a standard way
– A coordinate system that each color is represented by a single point.
• Most used models:
– RGB model (Monitor/TV)
– CMY model (3-color Printers)
– CMYK model (4-color Printers)
– HSI model (Color Image Processing and Description)
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Color Image Processing
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• RGB Color Model:
– Three Primary colors
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Color Image Processing
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• RGB Color Model
– Pixel Depth: The number of bits used to represent each pixel in RGB space.
– Full-color image: 24-bit RGB color image.
• (R, G, B) = (8 bits, 8 bits, 8 bits)
• Number of colors: 3
82 16,777,216
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Color Image Processing
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Color Image Generation
Color Image Acquisition
Filter-Sensor
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Color Image Processing
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• Safe RGB Colors: – Subset of colors is enough for some application
– Safe RGB colors (safe Web colors, safe browser colors)
– Only 6 levels of each primary colors are used.
– 63=216
– 000000=Black
– 111111=White!
– 110000=Purest Red
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Color Image Processing
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• Safe RGB Model
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Color Image Processing
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• Safe RGB Colors
Safe RGB Full Color RGB
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Color Image Processing
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• CMY and CMYK models: – CMY: Secondary colors of light, or primary colors
of pigments are used.
– Used to generate hardcopy output (Printer and Copier).
– Some facts: • Printer papers are white (reflect all colors)
• Printers use ink (Transparent)
• Cyan-Magenta-Yellow Pigments (ink) absorb Red-Green-Blue
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Color Image Processing
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• CMY and CMYK models:
– Printers: Overlapping layers of varying percentages of transparent cyan, magenta, and yellow inks.
– Light is transmitted through the inks and reflects off the surface below them.
– The percentages of CMY ink subtract inverse percentages of RGB from the reflected light so that we see a particular color
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• CMY and CMYK models:
– White Paper reflect 100% of incoming light.
– K (Black) is practical problem of C+M+Y≠Black (Muddy Brown). Add a fraction of Black color
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Color Image Processing
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• CMY and CMYK models:
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• CMY to RGB:
• RGB to CMYK:
1 1
1 1
1 1
C R R C
M G G M
Y B B Y
255255 2551. 255
255255 255
2. min , , 255255
C KCC R K
M G M KM
Y B K
Y KK C M Y YK
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Color Image Processing
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• HSI model:
– Human description of color is not RGB or CMYK
– Human description of color is Hue, Saturation and Brightness:
• Hue: color attribute
• Saturation: purity of color
• Brightness: achromatic notion of intensity
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Color Image Processing
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• RGB to HIS conversion Intensity Line (Equal RGB value)
Iso-Hue Triangle
Saturation (Distance)
Intensity
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Color Image Processing
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120º and 60º Distance (Pri. & Sec.)
Different Geometrical
Model is possible
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Color Image Processing
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• Single Hue
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• Split Channel
Hue Saturation Intensity
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RGB Hue
Saturation Intensity
• Channel Split
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Hue saturation
intensity Reconstructed
• Modifying Channel
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• Split Channel (RGB Model)
RGB R G B
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• Split Channel (CMYK Model)
CMYK C M Y K
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• Pseudo-color image processing
– Assign colors to gray values based on a specified criterion
– For human visualization and interpretation of gray-scale events
• Methods:
– Intensity slicing
– Gray level to color transformations
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• Gray Level Slicing
1, , ,k k kf x y c f x y G G
Color 2
Color 1
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• Gray Level Slicing (Two Levels)
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• Gray Level Slicing (More Levels)
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• Example (Nuclear Imaging) with 8 levels
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• Industrial Application
– Welding
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• Example:
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• Gray Level to Color Transform
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• Example
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• Example:
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• Combine several monochrome images:
– multi-spectral images (Remote Sensing/Medical)
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R G
B Near IR
R-G-B IR-G-B
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• Using Knowledge
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• Full-Color Image Processing
– Process each color components individually
– Process color vector directly
,
, ,
,
R x y
x y G x y
B x y
c
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• Color Transform
– Gray Level Transform:
– Color Image Transform:
• n=3 for RGB/HSI/CMY
• n=4 for CMYK
, ,g x y T f x y
1 2, , , , 1,2, ,i i ns T r r r i n
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• Intensity Magnification: • HSI:
• RGB:
• CMY:
, ,g x y T f x y
3 3 2 2 1 1, ,s kr s r s r
i is kr
1i is kr k
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• Sample Image:
– CMYK
– RGB
– HSI
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• Intensity Adjustment (k=0.7)
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• Color Complements:
– Analogy of Gray-Level Negative
– Color Complement: Hue directly opposite one another on color circle.
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Two different
approaches!
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• Color Slicing:
– Highlight a specific range of colors.
• Set non desired color to gray level.
0.5 2
O.W.
i i
i
i
r a Ws
r
2 2
0
1
0.5
O.W.
n
i i
ji
i
r a Rs
r
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W=0.2549 R0=0.1765
• Examples:
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Light
Flat
Dark
• Tonal Correction
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• Color Balancing:
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• Histogram Processing:
– Histogram Equalization may NOT apply independently!
– Logical approach:
• Uniform Intensity
• Hue unchanged
• Saturation may be changed or not!
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original
Intensity Equlization
Saturation Increasing
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• Color Image Smoothing:
– Like as Gray Level Images!
,
,
,
,
1ˆ , ,
,
xy
xy
xy
x y S
x y S
x y S
R x y
x y G x yMN
B x y
C
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Co
lor
Imag
e
R-C
han
ne
l
G-C
han
nel
B-C
han
nel
• RGB Components
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• HSI Components
H-Channel S-Channel I-Channel
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RGB Smoothing I- Smoothing Difference
• Two approached for smoothing:
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• Color Image Sharpening:
2
2 2
2
,
, ,
,
R x y
x y G x y
B x y
c
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RGB Sharpening I- Sharpening Difference
• Two approached for Sharpening:
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• Color Image Segmentation:
Will be discuss later!
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• Segmentation in Vector Space
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• Example:
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• Color Image Edge Detection:
– Previous Operator is not defined for vector space!
– Problem is more complicated!
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One change result two changes
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– New Gradients vectors:
2 2 2
2 2 2
, , and :unit vectors in R, G, and B axes.
,
xx
yy
T
xy
R G B R G B
x x x y y y
R G Bg
x x x
R G Bg
y y y
R R G G B Bg
x y x y x y
T
T
r g b
u = r g b v = r g b
u u
v v
u v
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Direction of maximum rate of changes of c(x,y) is:
1
0.5
21tan
2
Rate of change:
1cos 2 2 sin 2
2
xy
xx yy
xx yy xx yy xy
g
g g
F g g g g g
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new
Sum of previous gradients for RGB channel
Diff.
• Example:
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R-Grad G-Grad B-Grad
• R-G-B Gradients:
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• Noise
– Model are same as before
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• R-G-B Channels Corruptions
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Color Image Processing
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H-Channel S-Channel I-Channel
Noisy
Original
• HSI
ee.sharif.edu/~dip
E. Fatemizadeh, Sharif University of Technology, 2011
97
Digital Image Processing
Color Image Processing
97
• G- Channel Corruption
– Effect on all
Channels of
HSI
ee.sharif.edu/~dip
E. Fatemizadeh, Sharif University of Technology, 2011
98
Digital Image Processing
Color Image Processing
98
• Color Image Compression:
– Will be discuss later