Basf Coatings Gmbh

Guido Bischoff

Donald R Baughman

Stuart Kendall Scott

Matthew Leopold

Thomas Kantimm

Florian Steufmehl

Martin Schmitz

Jessica J Mcguckin

Markus Mundus

Carlos Vignolo

Benjamin Lanfer

Described herein is a computer-implemented method for matching and adjusting pigmentation of a target coating, the method including at least the steps of: retrieving from a formulation database which includes formulas for coating compositions with different pigmentations, one preliminary matching formula; providing a match metric; and by using one or more processors: calculating a match metric value for the preliminary matching formula; and deciding, based on a magnitude of the match metric value, whether the pigmentation is significantly improvable; in the case that the pigmentation is significantly improvable: defining a selection of at least one additional tinting base which might be eligible for further adjusting the preliminary matching formula to the target coating; calculating, for each of the at least one additional tinting base, a match metric value; and issuing, via an output device, a list of eligible additional tinting bases of the at least one additional tinting base.

Method and system for matching and adjusting pigmentation of a sample coating to a target coating

Described herein is a computer-implemented method, including: obtaining, using an image capturing device, at least one digital image, each digital image being obtained at a different angle with respect to a surface of a target coating; performing, using an electronic computer processor in an operative connection with at least one filtering unit, a first image analysis on the obtained at least one image to determine at least one bright region within the at least one image by isolating image foreground data from image background data; performing, using the processor, a blob analysis to determine at least one corrupt area within the at least one bright region; and performing additional steps if at least one corrupt area is found. Also described herein are a respective system and a respective computer-readable medium.

Method and system for defect detection in image data of a target coating

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

Described herein is a computer-implemented method for providing a match metric for matching and adjusting color of a target coating and at least one sample coating, the method including at least the steps of: obtaining, via at least one communications interface, spectral curve(s) of the target coating and spectral curves of the sample coating determined at at least one gloss geometry; producing normalized first derivative values of the normalized spectral curves of the target coating and of the sample coating; producing difference values between the normalized first derivative values of the target coating and of the sample coating; producing a first match metric for the target coating and the sample coating based at least on the difference values produced for the at least one gloss geometry; and producing the match metric based on the first match metric. Also described herein is a respective system.

Method and system for a roboust color matching and adjustment process of effect colors

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 computer-implemented method and a system for providing a match metric for quantifying a spectral similarity of a target coating and at least one sample coating, the system including a computing device that performs a computing process, the computing process including: receiving reflectance values of the target coating and the sample coating; normalizing each of the reflectance values; generating a normalized reflectance curve for the target coating and for the sample coating; producing derivative values of the normalized reflectance curve of the target coating and derivative values of the normalized reflectance curve of the sample coating with respect to the wavelength; producing difference values between the derivative values of the target coating and the derivative values of the sample coating; and producing a match metric for a similarity between the normalized reflectance curves of the target coating and the sample coating.

Method and system for quantifying a spectral similarity between a sample color and a target color

Described herein is a computer-implemented process which includes at least the following steps:

Method and device for detecting and adjusting decorative colour formulations in alignment with the visual perception of texture characteristics

Described herein is a method for generating a bi-directional texture function (BTF) of an object, the method including at least the following steps: Also described herein are respective systems for generating a bi-directional texture function of an object.

Generation of a bi-directional texture function

Described herein is a method for identifying an interference pigment in a coating. Also described herein is a corresponding device and a computer-readable medium.

Method and device for identifying interference pigments in a 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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Drensteinfurt, Germany

Guido Bischoff