Basf Coatings Gmbh

Donald R Baughman

Guido Bischoff

Stuart Kendall Scott

Matthew Leopold

Jessica J Mcguckin

Martin Schmitz

Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

Identification of effect pigments in a target coating

Disclosed herein is a method and a device that can measure an unknown target coating; can search, based on the measured data of the target coating, a database for one or more best matching coating formulas, i.e. one or more preliminary matching formulas, within the database; and that can refine the search using an image similarity metric between images of the one or more best matching coating formulas on the one side and images of the target coating on the other side, using deep learning techniques.

Method and device for deploying and using an image similarity metric with deep learning

Disclosed herein is a computer-implemented method, a respective device, and a non-transitory computer-readable medium. The method includes:

Method and device for identification of effect pigments in a target coating

Described herein is a method for predicting visual texture parameters of a paint having a known paint formulation. The visual texture parameters of the paint are determined using an artificial neural network on the basis of a number of color components used in the known paint formulation. The method includes determining a value of at least one characteristic variable describing at least one optical property using a physical model for the known paint formulation. The method also includes assigning the value to the known paint formulation, and transmitting the value to the artificial neural network as an input signal for determining the visual texture parameters. The value describes the at least one optical property for at least some of the number of color components of the known paint formulation. The method further includes training the neural network using a plurality of color originals each having a respective known paint formulation.

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Whitehouse, OH, United States of America

Donald R Baughman