US 12,352,627 B2
Method and device for deploying and using an image similarity metric with deep learning
Donald R Baughman, Whitehouse, OH (US); Matthew Leopold, Whitehouse, OH (US); Guido Bischoff, Muenster (DE); Stuart K Scott, Florham Park, NJ (US); and Jessica J Mcguckin, Whitehouse, OH (US)
Assigned to BASF COATINGS GMBH, Muenster (DE)
Appl. No. 17/905,062
Filed by BASF COATINGS GMBH, Münster (DE)
PCT Filed Feb. 26, 2021, PCT No. PCT/EP2021/054820
§ 371(c)(1), (2) Date Aug. 25, 2022,
PCT Pub. No. WO2021/170796, PCT Pub. Date Sep. 2, 2021.
Claims priority of application No. 20159532 (EP), filed on Feb. 26, 2020.
Prior Publication US 2023/0145070 A1, May 11, 2023
Int. Cl. G01J 3/46 (2006.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/24 (2022.01); G06V 10/74 (2022.01)
CPC G01J 3/463 (2013.01) [G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/242 (2022.01); G06V 10/761 (2022.01); G01J 2003/466 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method, the method comprising at least the following steps:
(a) obtaining, using at least one measuring device, for each of at least one measurement geometry color values, digital images and optionally texture values of at least one training target coating,
(b) providing a database which comprises formulas for coating compositions and, for each of the at least one measurement geometry, interrelated color values, interrelated digital images, and optionally interrelated texture values,
(c) retrieving, using a processor, from the database, for each training target coating, a list of a plurality of preliminary matching formulas based on the color values and optionally on the texture values obtained for the respective training target coating,
(d) dividing the list of the plurality of preliminary matching formulas into two sublists using visual inspection of the digital images of the at least one training target coating and the digital images interrelated with the preliminary matching formulas and retrieved from the database, wherein a first sublist comprises visually good matching formulas of the plurality of preliminary matching formulas and a second sublist comprises visually bad matching formulas of the plurality of preliminary matching formulas,
(e) creating, using the processor, for each training target coating, a plurality of triplets, each triplet comprising a digital image of the respective training target coating for one of the at least one measurement geometry, a digital image retrieved from the database for the one of the at least one measurement geometry which is interrelated with a visually good matching formula of the first sublist and a digital image retrieved from the database for the one of the at least one measurement geometry which is interrelated with a visually bad matching formula of the second sublist,
(f) training a convolutional neural network by providing the created triplets, one triplet after the other, to the convolutional neural network as a respective input, and optimizing a n-dimensional cost function, the cost function defining a similarity distance to the at least one training target coating, such that the cost function is minimized for the respective visually good matching formula and maximized for the respective visually bad matching formula, and
(g) making the trained neural network available in the processor for ranking of a digital image of a coating composition with respect to a digital image of a target coating.