Abstract: Described herein are methods and systems for matching the color of a sample coating to the color of a reference coating. More specifically, described herein are methods and systems for determining an adapted modified sample coating formulation which, when applied onto a substrate, sufficiently matches the color of a reference coating by determining adjusted optical data for each individual color component present within the sample coating formulation and using the adjusted optical data to determine the modified sample coating formulation. Use of the adjusted optical data for each individual color component present within the sample coating formulation within the color adjustment process allows to compensate variations of colorant strength characteristics of colorants in paint production.

Abstract: 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.

Abstract: Disclosed herein is a computer-implemented method, a respective device, and a non-transitory computer-readable medium. The method includes: obtaining color values, texture values and digital images of a target coating, retrieving from a database one or more preliminary matching formulas based on the color and/or texture values obtained for the target coating, determining sparkle points within the respective obtained images and within the respective images associated with the one or more preliminary matching formulas, creating subimages of each sparkle point from the respective images, providing the created subimages to a convolutional neural network, the convolutional neural network being trained to correlate a respective subimage of a respective sparkle point with a pigment and/or pigment class, and determining, based on an output of the neural network, at least one of the one or more preliminary matching formulas as the formula(s) best matching the target coating.

Abstract: 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.

Abstract: Described herein is a computer-implemented process which includes at least the following steps: receiving at least one image or a plurality of images, implementing an image analysis for each of the images obtained, using at least one processor, identifying at least one sparkle point within a respective image, implementing a features analysis of the at least one identified sparkle point in the respective image in respect of at least one predefined size feature, determining at least one value for the at least one predefined size feature for the at least one sparkle point, calculating a size distribution for the respective image based on the determined value for the at least one predefined size feature, and providing a formulation for a coating which is identical or at least similar in appearance to the target coating.

Abstract: Described herein is a method for generating a bi-directional texture function (BTF) of an object, the method including at least the following steps: measuring an initial BTF for the object using a camera-based measurement device, capturing spectral reflectance data for the object for a pre-given number of different measurement geometries using a spectrophotometer, and adapting the initial BTF to the captured spectral reflectance data), thus, gaining an optimized BTF. Also described herein are respective systems for generating a bi-directional texture function of an object.

Abstract: Disclosed herein are methods and systems for generating display images of effect coatings. Further disclosed herein are methods and systems for ad-hoc generation of high quality images displaying the color as well as the texture of effect coating(s) without the use of rendering techniques using predefined illumination conditions and object data of virtual objects. The generated display images are especially suitable for assessing characteristics of effect coating(s) or for assessing color differences between two or more effect coating(s) based on the generated display images by arranging them side by side in horizontal order. It is also possible to transpose the display images by swapping the x- and y-axis of the images such that an optimized arrangement in vertical order, e. g. for mobile devices, is obtained.

Abstract: 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.

Abstract: Disclosed herein is a computer-implemented method for providing application adaption parameters to compensate for an influence of a given target paint application process on a color matching method and/or to consider an influence of a given target paint application process within a color predicting method.

Abstract: Disclosed herein is a computer-implemented color matching method using a paint adjustment algorithm running on a processor and a database which includes specific optical data of individual color components Further disclosed herein is a respective system.

Abstract: Disclosed herein is a computer-implemented method for a color matching process where an offset is determined that is relieved of an application process bias. Further disclosed herein is a respective system.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein is a computer-implemented method, a respective device, and a non-transitory computer-readable medium. The method includes: obtaining color values, texture values and digital images of a target coating, retrieving from a database one or more preliminary matching formulas based on the color and/or texture values obtained for the target coating, determining sparkle points within the respective obtained images and within the respective images associated with the one or more preliminary matching formulas, creating subimages of each sparkle point from the respective images, providing the created subimages to a convolutional neural network, the convolutional neural network being trained to correlate a respective subimage of a respective sparkle point with a pigment and/or pigment class, and determining, based on an output of the neural network, at least one of the one or more preliminary matching formulas as the formula(s) best matching the target coating.

Abstract: Disclosed herein is a computer-implemented method for generating a coating formulation that yields a coating which is, to the eye of a human observer, identical or substantially similar in appearance to a target coating, as well as a device for performing the method.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Described herein is a method for generating a bi-directional texture function (BTF) of an object, the method including at least the following steps: measuring an initial BTF for the object using a camera-based measurement device, capturing spectral reflectance data for the object for a pre-given number of different measurement geometries using a spectrophotometer, and adapting the initial BTF to the captured spectral reflectance data), thus, gaining an optimized BTF. Also described herein are respective systems for generating a bi-directional texture function of an object.