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Title: A methodology for photometric validation in vehicles visual interactive systems.
Authors: Faria, Alexandre Wagner Chagas
Menotti, David
Pappa, Gisele Lobo
Lara, Daniel da Silva Diogo
Araújo, Arnaldo de Albuquerque
Keywords: Image intensity
Pattern recognition
Issue Date: 2012
Citation: FARIA, A. W. C. et al. A methodology for photometric validation in vehicles visual interactive systems. Expert Systems with Applications, v. 39, n. 4, p. 4122-4134, 2012. Disponível em: <>. Acesso em: 29 nov. 2012.
Abstract: This work proposes a methodology for automatically validating the internal lighting system of an automobile by assessing the visual quality of each instrument in an instrument cluster (IC) (i.e., vehicle gauges, such as speedometer, tachometer, temperature and fuel gauges) based on the user’s perceptions. Although the visual quality assessment of an instrument is a subjective matter, it is also influenced by some of its photometric features, such as the light intensity distribution. This work presents a methodology for identifying and quantifying non-homogeneous regions in the lighting distribution of these instruments, starting from a digital image. In order to accomplish this task, a set of 107 digital images of instruments were acquired and preprocessed, identifying a set of instrument regions. These instruments were also evaluated by common drivers and specialists to identify their non-homogenous regions. Then, for each region, we extracted a set of homogeneity descriptors, and also proposed a relational descriptor to study the homogeneity influence of a region in the whole instrument. These descriptors were associated with the results of the manual labeling, and given to two machine learning algorithms, which were trained to identify a region as being homogeneous or not. Experiments showed that the proposed methodology obtained an overall precision above 94% for both regions and instrument classifications. Finally, a meticulous analysis of the users’ and specialist’s image evaluations is performed
ISSN: 09574174
metadata.dc.rights.license: O periódico Expert Systems with Applications concede permissão para depósito do artigo no Repositório Institucional da UFOP. Número da licença: 3305300363751.
Appears in Collections:DECOM - Artigos publicados em periódicos

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