Machine Learning
Computer Vision
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)
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
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
Document Image Processing for Paper Side Communications
IEEE Transactions on Multimedia
Integration of nomadic devices with automotive user interfaces
IEEE Transactions on Consumer Electronics
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
A methodology for photometric validation in vehicles visual interactive systems
Expert Systems with Applications: An International Journal
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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%.