Affiliations: [a] Computer Architecture and Technology Department, University of Granada | [b] Optics Department, University of Granada | [c] Stomatology Department, University of Granada | [x] Department of Computer Science and Artificial Intelligence, CITIC-UGR, Universidad de Granada, Granada, Spain | [y] Department of Mathematics and Computer Science, University of Salerno, Salerno, Italy | [z] Department of Information Engineering, University of Pisa, Pisa, Italy
Abstract: Color change modelling is an essential problem in a wide range of colorimetric applications. Specifically, the approximation to the color expected after a physical chemical or natural color is essential in industry and in color science in general. This work proposes a modified neuro-fuzzy approach as a solution for a color change modelling problem. Neuro-fuzzy systems are well known methods for data modelling; their main advantage is their ability to provide an accurate solution from which an interpretable set of rules that can be extracted and used by the scientists. However these models have the problem that the global approximation optimization can lead to a deficient interpretation of the rules extracted from the model. This work proposes a modified neuro-fuzzy model that performs a simultaneous global and local modelling; this property is reached thanks to a special partitioning of the input space in the system. Specifically, the proposed methodology will be applied to a very important problem from the clinical and odontologic point of view as it is the modelling of the tooth color variation after a bleaching process. The availability of tools that help to predict these changes, from the initial chromaticity of the tooth, can solve the problem of lack of information on the expected tooth color after a specific treatment and help the dentist in the decision making on the most appropriate protocol for this treatment and in providing adequate information to the patient.
