PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Support Vector Machines for color adjustment in automotive basecoat
Francisco Ruiz, Cecilio Angulo and Núria Agell
In: Artificial Intelligence Research and Development Frontiers in Artificial Intelligence and Applications , 146 (146). (2006) IOS Press , Nederland , pp. 19-27. ISBN 1586036637

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.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2940
Deposited By:Cecilio Angulo
Deposited On:26 December 2006