Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
The Defect Detection Using Human Visual System and Wavelet Transform in TFT-LCD Image
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
A new methodology for photometric validation in vehicles visual interactive systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Document Image Processing for Paper Side Communications
IEEE Transactions on Multimedia
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
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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.