A new methodology for photometric validation in vehicles visual interactive systems

Abstract

This work proposes a new methodology for automatically validating the internal lighting system of an automotive, i.e., assessing the visual quality of an instrument cluster (IC) from the point of view of the user. Although the evaluation of the visual quality of a component is a subjective matter, it is highly influenced by some photometric features of the component, such as the light intensity distribution. The methodology proposed here uses this last photometric feature to classify regions in images of instrument cluster components as homogenous or not, while also taking into account the user subjective evaluation. In order to achieve that, we acquired a set of 107 IC component images, and preprocessed them. These same components were evaluated by a user to identify their non-homogenous regions. Then, for each component region, we extracted a set of homogeneity descriptors. These descriptors were associated with the results of the user evaluation, and given to two machine learning algorithms. These algorithms were trained to identify a region as homogenous or not, and showed that the proposed methodology obtains precision above 95%.

A methodology for photometric validation in vehicles visual interactive systems

“…Furthermore, we are also interested in the impact each region has in the global uniformity (GU) of the entire component. Hence, we propose a descriptor that considers both the local region and global component intensities, named as Relative Descriptor (Faria et al, 2010) and defined as…”

“…A preliminary version of this work appears in Faria, Menotti, Lara, Pappa, and Araújo (2010), where the methodology was first introduced. However, in Faria et al (2010), a single dataset obtained from the classification of a specialist was given as input to two machine learning algorithms. Here, we extend the set of experiments so that six different datasets are used.…”