Chapter 6 Color image processing

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Chapter 6 Color image processing

• 6.1 Color Fundamental
• 6.2 Color models
• 6.3 Pseudo color processing
• 6.4 Basics of full-color image processing
• 6.5 Color transformation
• 6.6 Smoothing and sharpening

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• Two major areas full color and pseudo color
• 6.1 Color fundamentals
• Primary color of light R, G, B
• Secondary color of light magenta, cyan and
  yellow
• Pigment or colorant subtract or absorbs a
  primary color
• of light and reflect or transmit the other two
• HIS model (a color may be characterized by its
  chromaticity and brightness)
• Chromaticity Hue and saturation taken together
• Hue is a dominant color as perceived by a
  observer
• Saturation is the relative purity or the amount
  of white light
• mixed with a hue

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• Brightness embody the chromatic notion of
  intensity
• Three basic properties used to describe the
  quality
• of chromatic light source source, radiance, and
  brightness
• Tri-stimulus values X, Y and Z
• Tri-chromatic coefficients
• CIE chromaticity diagram
• Has superior performance over other color
  transforms especially in clustering of color
  distribution and estimate of color difference
• Shows color as a function of x (red) and y
  (green)
• Useful for color mixing
• Boundary of the diagram shows fully saturated
• Equal energy point
• CIE color models include CIE XYZ, CIE x,yY, CILE
  Lab, and CIE Luv

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6.2 Color models (Color space or color system)

• Purpose to facilitate the specification of
  colors in some standard
• A specification of a coordinate system and a
  subspace within that system where each color is
  represented a single point
• Two applied directions for color models hardware
  and applications where color manipulation (color
  graphics)
• RGB models--color monitors
• CMY (CMYK)--color model for color printing
• YIQcolor model for color television
• HIS a color model for humans to describe and to
  interpret color decouple the color and
  gray-level information

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• The RGB Color model
• Have a depth of 24bits
• The CMY color model
• The YIQ model
• The HIS color model
• Hue a color attributes that describes a pure
• Saturation a measure of the degree to which a a
  pure color is diluted by a color
• Brightness-- is a subjective descriptor that is
  practically impossible to measure 0
• HIS is an ideal tool for developing image
  processing algorithm

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• Converting colors from RGB to HIS
• Converting colors from HIS to RGB
• multiply H by 360o
• (1) When H is in RG sector (0? H ?120), the RGB
  components are given by (Eq. 6.2-5 6.2-7)

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• (2) H is in GB sector (120? H ?240) Eq. 6.2-8
  6.2-11
• (3) H is in BR sector (240? H ?360) Eq. 6.2-12
  6.2-15
• Manipulating HIS component images
• Change the individual color of any region in the
  RGB image
• 6.3 Pseudo color (False color) image processing
• Differentiate the process of assigning colors to
  monochrome images from the process associated
  with true color images
• Assign colors to monochrome images from the
  process associated with true color images
• To illustrate the power to allow independent
  control over HIS (Fig. 6.17)

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6.3.1 Intensity slicing (Color coding)

• If an image is interpreted as a 3-D function, the
  method can be viewed as one of placing planes
  parallel to the coordinate plane of the image
• Each plane slices the function in the area of
  intersection
• Two color images whose relative appearance can be
  controlled by moving the slicing plane up and
  down the gray-level axis
• Gray-level to color assignments are made
  according to the relation f(x,y) ck if
  f(x,y) ?? Vk (P planes, P1 intervals)
• Plane is useful for geometric interpretation of
  the intensity slicing technique

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• The gray scale was divided into intervals and a
  different color was assigned to each region (Fig.
  6.20)
• Is simple but powerful aid in visualization,
  especially if numerous images are involved

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6.3.2 Gray level to color transformation

• Achieve a wide range of pseudo color enhancement
• Perform three independent transformations on the
  gray levels of any input pixels these results
  are fed separately into the red, green, and blue
  channels of a color television monitor. (Fig.
  6.23)
• Can be based on smooth, nonlinear functions,
  which, as might be expected (based on a single
  monochrome image)
• Piecewise linear function (Fig. 6.19)
• Obtain various degrees of enhancement (Fig.6.24)
• Changing the phase and frequency of each sinusoid
  can emphasize ranges in the gray scale

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Combine several monochrome images into a single
color composite (Fig . 6.26)

• Used for multi-spectral image processing
  (different sensors produces individual monochrome
  images)
• Difference in color in various parts of the
  Potomac River (Fig. 6.27a)
• This processing help visualize events of interest
  in complex images, especially when those events
  beyond our normal sensing capabilities

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6.4 Basics of full color image processing

• Two categories
• (1) Process each component individually and then
  form a composite processed image from the
  individually processed components
• (2) Work with color pixels directly
• Color pixel are vectors
• Let C be an arbitrary vector
• Two conditions must be satisfied for
  pre-component and vector-based
• Has to be applicable to both vectors and scalars
• The operator must be independent of the other
  components

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6.5 Color transformations (Interactive
enhancement)

• Formulation of color transformation
• g(x,y)Tf(x,y)
• Color transformation (color mapping) of the form
  si Ti r1 , r 2,,rn (n number of components)
• For ex modify the intensity of the image on Fig.
  6.30(a) g(x,y)kf(x,y)
• In HIS color space, this can be done with s3kr3
• Only intensity component must be transformed
• Few operations but the conversion calculation are
  more computationally intense

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• In RGB space, this can be done with three
  components sik ri (i1,2,3.)
• In CMYK space, it requires a set of linear
  transformations (6.5-6)
• Converting between representations must be
  factored into the decision regarding the color
  space in which to implement
• Color complements
• Hue directly opposite one another on the color
  circle
• Analogous to the gray-level negatives
• Useful for enhancing details that is embedded in
  dark region of a color image

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6.5.3 Color slicing

• Highlighting a specific range of colors two
  approaches
• display the colors of interest
• Use the region defined by the colors as a mask
  for further processing
• Require each pixels transformed color components
  to be a function of all n original pixels
  components
• slice a color image map the colors outside
  some range of interest to a non-prominent neutral
  color

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• Method 1 enclosed by a cube (a cube with width
  W and centered at a prototypical color with
  components)
• Highlight the colors around the prototype by
  forcing all other colors to the midpoint of the
  reference color space
• Method2 enclosed by a sphere

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6.5.4 Tone and color correction

• Turn a PC into a digital darkroom
• The effectiveness of the transformations is
  judged ultimately in print
• Transformations are developed, refined, and
  evaluated on monitors
• Maintain a high degree of color consistency
  between the monitor and the eventual output
  device (device independent color model that
  relates the color gamuts)

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• The success is a function of the quality of the
  color profiles used to map each device to the
  model and the model itself (CIE Lab model)-
• Lab is useful for color management system (CMS)
• Lab is color metric (color perceived as
  matching are encoded identically), perceptually
  uniform (color differences among various hues are
  perceived uniformly) and device independent

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• Its gamut encompasses the entire visible
  spectrum, and can represent accurately the colors
  of any display, print, or input device
• useful in manipulation and image compression
  applications
• Lab is an excellent de-coupler of intensity and
  color
• The principal benefit of a calibrated imaging
  system
• allow tonal and color imbalances to be corrected
  interactively and independently

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Corrections of Tonal ranges

• Key type (tonal range of an image)-refers to its
  general distribution of color intensities
• High-key image is concentrated at high
  intensities
• Low-key image are located at low intensities
• It is desirable to distribute the intensities of
  a color image equally between the highlight and
  shadow area
• Modifying tones normally are selected
  interactively
• Operation adjust the images brightness and
  contrast over a suitable range of intensities
• the colors are not changed.
• In RGB and CMYK map all three components
• In HIS only intensity component is modified

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• Color imbalanceanalyzing with color spectrometer
• color wheel can be used to predict how one
  component will affect another
• The perception of one color is affected by its
  surrounding colors
• The portions of any color can be increased by
  decreasing the amount of the opposite
• Transformations are the functions required for
  correcting the images (Fig. 6.36)

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6.5.5 Histogram processing

• The gray-level processing can be applied to color
  images in an automatic way
• Produce an image with a uniform histogram of
  intensity values
• To histogram equalize the components of a color
  image individually is unwise (results in
  erroneous color)
• A more logical approach spread the color
  intensities uniformly (use HIS space), leaving
  the colors themselves unchanged

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6.6 smoothing and sharpening

• Modify values based on the characteristics of the
  surrounding (without regard to its neighbors in
  the previous section)
• Instead of scalar gray-level values deal with
  vectors
• 6.6.1 Color image smoothing (Eq. 6.6-1 and 6.6-2)
• can be carried out on a per-color-plane basis
• smooth only the intensity component of the HIS
  representation and convert the processed result
  to an RGB image for display
• 6.6.2 Color image sharpening
• Laplacian of vector c in RGB, and in I Component
  of HSI

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6.7 Color segmentationsegment objects of a
specified color range

• 6.7.1 Segmentation in HIS color space
• Carry out the segmentation process on individual
  planes
• color is conveniently represented in the hue
  image
• saturation is used as a masking image
• intensity is used less frequently for
  segmentation (carry no color information)
• 6.72. Segmentation in RGB vector space
• Better results generally are obtained
• Obtain an estimate of the average color vector
  a
• Classify each RGB pixel in a given image as
  having a color in the specified range or not
  have a measure of similarity (Euclidean distance)

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• D(z,a) z-a
• Distance measure with the covariance matrix
• D(z,a) D0
• Implementing (6-.7-1 and 6.7-2) is
  computationally expensive
• Distance measure with bounding boxa compromise
• Dimension is proportional to the standard
  deviation of the sample along each of the axis
• standard deviation is calculated using sample
  color data
• Segment it by determining whether or not it is on
  the surface or inside the boxsimpler
  computationally when compared to a spherical or
  elliptical enclosure
• Fig6.44b Yield much more accurate results
  compared to HIS (correspond much more closely
  with what we would define as reddish

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6.7.3 Color edge detection

• Computing the gradient on individual images and
  then using the results to form a color image will
  lead to erroneous results
• Individual based and vector-space based computing
  depend on accuracy or just detecting edges (Fig.
  6.45)
• Extend the concept of a gradient from a scalar
  function to vector functions

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• The def. of a gradient based on a vector (is a
  vector pointing in the direction of maximum rate
  of change f at (x,y)
• Let r, g, and b be the unit vector along the R,G,
  and B axis of RGB color space
• 
  and
• The direction of maximum rate of change of C(x,y)
  is given by
• The value of the rate of changes at (x,y) in the
  direction is given by
• gxxu.u ,gyyv.v, gxyu.v

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6.8 The noise in color images

• Different noise levels are more likely to be
  caused by differences in the relative strength of
  illumination available to each if the color
  channels
• CCD sensors are noisier at low level of
  illumination
• Intensity component is slightly smoother than any
  of the three noisy images (because intensity
  image is the average of the RGB images)

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6.9 Color image compression

• The data that are the object of any compression
  are the components of each color pixel
• Compression is the process of reducing and
  eliminating redundant and/or irrelevant data

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