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Automatic Color Gamut Calibration. Cristobal Alvarez-Russell Michael Novitzky Phillip Marks. Inspiration. G. Klein and D. Murray, Compositing for Small Cameras , ISMAR'08. Motivation. Calibrate and compensate for: Color distortions of a small video camera

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automatic color gamut calibration

Automatic Color Gamut Calibration

Cristobal Alvarez-Russell

Michael Novitzky

Phillip Marks

  • G. Klein and D. Murray, Compositing for Small Cameras, ISMAR'08
  • Calibrate and compensate for:
    • Color distortions of a small video camera
    • Lighting conditions of environment
  • Purpose:
    • Augmented Reality
      • Matching the color gamut of virtual objects to video camera image
    • Robotics
      • Calibrating a video camera for particle-filter-based object tracking (i.e. orange ball in robot soccer)
  • GretagMacbeth ColorChart
    • Diffuse material
    • Color samples under daylight
    • RGB values are known
approach cont
Approach (cont.)
  • Start with picture of a scene with the chart
  • Locate the squares of the chart in the image
  • Unproject and crop the chart
  • Sample the colors in the chart
  • Adjust the color of the entire image (and subsequent ones)
locating the chart
Locating the Chart
  • Failed Attempts
    • Swain’s Histogram Back-projection
      • Color constancy a big problem
        • Tried color constancy approximations
      • Not good for color chart
        • Too many histogram matches -> false positives
      • Only returned a point within the square
        • We hoped it would be an estimation of the center of the chart
        • No information useful for unprojection
locating the chart cont1
Locating the Chart (cont.)
  • Color constancy
    • Color normalization
locating the chart cont2
Locating the Chart (cont.)
  • False positives
    • Ratios high because of wide chart histogram
locating the chart cont3
Locating the Chart (cont.)
  • Result not useful for feature extraction
    • Not a good estimate of the center of the chart
locating the chart cont5
Locating the Chart (cont.)
  • Current approach
    • First step: User interface
      • User clicks and labels squares
      • Flood fill
        • Uses histogram
      • Create screen-aligned bounding box
locating the chart cont9
Locating the Chart (cont.)
  • Second step: Connected components
    • Sweep through the image
    • Label neighboring pixels that are activated
    • Choose the connected component with the highest vote
locating the chart cont12
Locating the Chart (cont.)
  • Third step: Recognize regions
    • We need to find the corners of the region within the bounding box
    • First attempt: Draw lines from bounding box corners and vote on likelihood of region edge
      • Failed!
    • Second attempt: Look for region corner iteratively from bounding box corner
      • Success!
unprojecting the chart
Unprojecting the chart
  • Start with corners of some color regions
  • Construct a matrix A composed of image and world point correspondences
  • Compute homography matrix from null space of A
    • SVD to compute it
  • Use inverse homography to unproject each pixel
unprojecting the chart cont
Unprojecting the chart (cont.)
  • Problems:
    • OpenCV matrices are not good for numerical methods
      • Switched to GSL
    • Noise in region corner positions
      • Remove smallest eigenvalue of singular matrix
      • Squares in the middle of the chart better
sampling the chart
Sampling the chart
  • We sample at square centers
    • Squares centers estimated by predefined, specific ratios of the chart
    • We assume the homography and the unprojection are good enough
  • Stochastic sampling
    • We average several samples to reduce noise influence
adjusting the color gamut
Adjusting the color gamut
  • Step 1: Adjust white balance of the samples
    • Simple linear scale
    • Using White 9.5 and Black 32 from color chart
      • Both in chart in image and known RGB values
adjusting the color gamut cont2
Adjusting the color gamut (cont.)
  • Step 2: Adjust chromaticity
    • Use color samples as a distribution
      • Linear scale of every pixel color according to mean and standard deviation of distribution
      • Color samples from chart do not map to themselves
    • Approach 1: Marginal Distribution
      • Three 1D distributions (one per channel)
      • Treat channels independently from each other
    • Approach 2: Joint Distribution
      • Treat colors as 3D points in RGB cube
      • Standard deviation is a 3D distance from the mean color
future work
Future Work
  • Locating the color chart
    • Use SIFT-like descriptors with point matching according to the color chart structure
    • Use grid-pattern algorithms like the ones used in fiducial-based tracking (i.e. ARToolkit)
  • Chart unprojection
    • Try iterative homography estimation
  • Color gamut adjustment:
    • Interpolate colors using a tetrahedral mesh
    • Try using color spaces that separate chromaticity from intensity (HSV, YUV, etc.)