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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 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 • 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)
Approach • GretagMacbeth ColorChart • Diffuse material • Color samples under daylight • RGB values are known
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 • 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 (cont.) • Original image
Locating the Chart (cont.) • Color constancy • Color normalization
Locating the Chart (cont.) • False positives • Ratios high because of wide chart histogram
Locating the Chart (cont.) • Result not useful for feature extraction • Not a good estimate of the center of the chart
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 (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 (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 • 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.) • 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 • 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 • 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 (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 • 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.)
