Color and Appearance Technology, Inc.

David L Alston

Kevin B Kern

Mark Jarvis

Walter Borys

A color and appearance measurement apparatus combines a computer controlled color video camera, controlled illumination conditions, frame grabber to digitize the video signal, and image processing methods to perform non-contact calorimetric measurements of complex patterns of color. Video images of samples placed into the field of view of the camera are digitized and converted from the color camera's red, green, and blue (RGB) color space to the internationally recognized and standardized CIE XYZ color space. A calibration process to set the dynamic range of the camera for the given illumination, correct for spatial non-uniformities in the lighting and responsivity of the detector arrays, correct for temporal variations in the lighting intensity, and determine the necessary coefficients to transform the RGB values to CIE XYZ tristimulus values ensures the measurements are highly repeatable and that measured color and appearance differences are in agreement with visual observations. Visual appearance attributes of the samples such as color, texture, and gloss are measured and evaluated. These measurements provide quantifiable metrics, to which various color and appearance attributes of other samples can be compared, for quality control inspection of complex colored samples that cannot be measured using conventional color measuring instruments.

Colorimetric imaging system for measuring color and appearance

A color measuring system uses fuzzy logic to establish acceptable pass/fail tolerances for production or inventory samples. The tolerances define a super ellipsoid which has six individual tolerance coordinates. Initially, the tolerances are defined by the high/low values of the pass samples in a training set which have been chosen to represent a population of visually rated pass and fail samples. A classifier template uses the super ellipsoid tolerances and ranks every sample in the training set. Any pass/fail ranking that differs from the visual rank is considered an error. All of the error distances are summed and all samples are passed through the fuzzy logic contained in an induction module to determine the changes needed in the super ellipsoid tolerances to minimize the error distance. The new tolerances are then passed to the classifier template for a new ranking of the training samples. The process is repeated until a minimum error distance is achieved. The final tolerances are then used by the classifier to rank subsequent samples as either pass or fail. Shade sorting can also be accomplished. In that case, the training samples are rated twice, first for the centroid color differences and then for the shade differences. The training set is processed by the classifier template/induction module to determine the two tolerances. The classifier then rates production/inventory samples to determine if they meet the centroid color needs, and then places the samples in the proper shade sort group.

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Holland, PA, United States of America

David L Alston