Support Vector Machines for color adjustment in automotive basecoat

Francisco Ruiz, Cecilio Angulo, Núria Agell

Producció científica: Capítol de llibreContribució a una conferènciaAvaluat per experts

Resum

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.

Idioma originalAnglès
Títol de la publicacióArtificial Intelligence Research and Development
Pàgines19-27
Nombre de pàgines9
Estat de la publicacióPublicada - 2006
Publicat externament
Esdeveniment9th International Conference of the Catalane Association for Artificial Intelligence, CCIA 2006 - Perpignan, France
Durada: 26 d’oct. 200627 d’oct. 2006

Sèrie de publicacions

NomFrontiers in Artificial Intelligence and Applications
Volum146
ISSN (imprès)0922-6389

Conferència

Conferència9th International Conference of the Catalane Association for Artificial Intelligence, CCIA 2006
País/TerritoriFrance
CiutatPerpignan
Període26/10/0627/10/06

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