Name
Abstract | ||
|---|---|---|
Traditionally, Computer Colorant Formulation has been implemented using a theory of radiation transfer known as the Kubelka-Munk (K-M) theory. In recent studies, Artificial Neural Networks (ANNs) has been put forward for dealing with color formulation problems. This paper investigates the ability of Support Vector Machines (SVMs), a particular machine learning technique, to help color adjustment processing in the automotive industry. Imitating 'color matcher' employees, SVMs based on a standard Gaussian kernel are used in an iterative color matching procedure. Two experiments were carried out to validate our proposal, the first considering objective color measurements as output in the training set, and a second where expert criterion was used to assign the output. The comparison of the two experiments reveals some insights about the complexity of the color adjustment analysis and suggests the viability of the method presented. |
Year | Venue | Keywords |
|---|---|---|
2006 | CCIA | color matcher,color adjustment processing,automotive industry,support vector machines,color formulation problem,color adjustment analysis,artificial neural networks,computer colorant formulation,iterative color,automotive basecoat,objective color measurement,artificial neural network,machine learning,support vector machine,decision support system,gaussian kernel,artificial intelligent |
Field | DocType | Volume |
Training set,Computer science,Support vector machine,Decision support system,Artificial intelligence,Artificial neural network,Gaussian function,Machine learning,Automotive industry | Conference | 146 |
ISSN | ISBN | Citations |
0922-6389 | 1-58603-663-7 | 1 |
PageRank | References | Authors |
0.39 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Ruiz | 1 | 301 | 29.12 |
| Cecilio Angulo | 2 | 434 | 57.48 |
| Núria Agell | 3 | 199 | 30.62 |