Computer-implemented neural network color matching formulation applications

• US 6,804,390 B2
• Filed: 02/07/2001
• Issued: 10/12/2004
• Est. Priority Date: 02/07/2001
• Status: Expired due to Term

• Current Assignee: BASF Corporation (BASF SE)
• Original Assignee: BASF Corporation (BASF SE)
• Inventors: McClanahan, Craig J.
• Primary Examiner(s): Bali, Vikkram
• Application Number:
  US09/779,101
• Publication Number:
  US 20020106121A1
• Time in Patent Office: 1,343 Days
• Field of Search: 382/128, 382/156, 382/157, 382/158, 706/15, 706/16, 706/26, 345/604
• US Class Current: 382/156
• CPC Class Codes: G01J 3/46 Measurement of colour; Colour measuring devices, e.g. colorimeters measuring colour temperature G01J5/60
• CPC Class Codes: G01J 3/462 Computing operations in or between colour spaces; Colour management systems
• CPC Class Codes: G01J 3/463 Colour matching
• CPC Class Codes: G01J 3/504 Goniometric colour measurements, for example measurements of metallic or flake based paints
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30156 Vehicle coating
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: H04N 1/60 Colour correction or control H04N1/54 takes precedence
• CPC Class Codes: H04N 1/603 controlled by characteristics of the picture signal generator or the picture reproducer

Abstract

A method and apparatus for color matching are provided, in which paint recipe neural networks are utilized. The color of a standard is expressed as color values. The neural network includes an input layer having nodes for receiving input data related to paint bases. Weighted connections connect to the nodes of the input layer and have coefficients for weighting the input data. An output layer having nodes are either directly or indirectly connected to the weighted connections and generates output data related to color values. The data to the input layer and the data from the output layer are interrelated through the neural network'"'"'s nonlinear relationship. The paint color matching neural network can be used for, but not limited to, color formula correction, matching from scratch, effect pigment identification, selection of targets for color tools, searching existing formulas for the closest match, identification of formula mistakes, development of color tolerances and enhancing conversion routines.

Claims

1. A color retrieval neural network for retrieving standards from a database of standards based upon color values of a sample, said color retrieval neural network comprising:

an input layer having input nodes for receiving input data related to the color values of the sample;

an output layer having output nodes for providing output data related to the standards in the database;

first weighted connections connected to said nodes of said input layer, each of said first weighted connections having a coefficient for weighting said input data; and

wherein a non-linear relationship exists between said input data to the input layer and output data from the output layer, the database including color values associated with a plurality of angles for each standard in the database, said non-linear relationship interrelating color values of the sample at different angles with the color values associated with the different angles of at least one of the standards in the database.

2. The color retrieval neural network of claim 1 further comprising:

at least one hidden layer having nodes connected to said first weighted connections, said hidden layer being interposed between said input and output layers; and

second weighted connections connected to said hidden layer nodes and to said output layer nodes, each of said second weighted connections having a coefficient for weighting said outputs of said hidden layer nodes.

3. The color retrieval neural network of claim 1 wherein the input data is based on at least one of different angles.

4. The color retrieval neural network of claim 3 wherein a color measuring device measures the color values of the sample at different angles.

5. The color retrieval neural network of claim 3 wherein the color values are at angles selected from the group consisting of 15 degrees, 20 degrees, 45 degrees, 75 degrees, 110 degrees and a combination thereof.

6. The color retrieval neural network of claim 3 wherein the color values are color values at a flop angle.

7. The color retrieval neural network of claim 3 wherein the color values are at angles of 25 degrees, 45 degrees, and a flop angle.

8. The color retrieval neural network of claim 3 wherein the color values are at angles that are less than 30 degrees.

9. The color retrieval neural network of claim 3 wherein each dimension of the three-dimensional coordinate system is indicative of a color attribute.

10. The color retrieval neural network of claim 3 wherein the color values are three-dimensional with a coordinate system.

11. The color retrieval neural network of claim 10 wherein the first dimension of the three-dimensional coordinate system is indicative of lightness-darkness color attribute, wherein the second dimension of the three-dimensional coordinate system is indicative of redness-blueness color attribute, wherein the third dimension of the three-dimensional coordinate system is indicative of yellowness-greenness color attribute.

12. The color retrieval neural network of claim 10 wherein the three-dimensional coordinate system is an L*, a*, b* color coordinate system.

13. The color retrieval neural network of claim 10 wherein the three-dimensional coordinate system is a cylindrical color coordinate system.

14. The color retrieval neural network of claim 10 wherein the three-dimensional coordinate system is a spherical color coordinate system.

15. The color retrieval neural network of claim 10 wherein the three-dimensional coordinate system is a Cartesian coordinate system.

16. The color retrieval neural network of claim 1 wherein the output layer is a processing layer having processing nodes such that a self-organizing map network is formed.

17. The color retrieval neural network of claim 1 wherein the input layer, the first weighted connections and the output layer represent a color space of the database of standards.