Browsing by Author "Lara, Daniel da Silva Diogo"

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    A methodology for photometric validation in vehicles visual interactive systems.
    (2012) Faria, Alexandre Wagner Chagas; Menotti, David; Pappa, Gisele Lobo; Lara, Daniel da Silva Diogo; Araújo, Arnaldo de Albuquerque
    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
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    Uma metodologia para validação fotométrica em sistemas interativos visuais baseada em inteligência computacional.
    (2009) Faria, Alexandre Wagner Chagas; Lara, Daniel da Silva Diogo; Araújo, Arnaldo de Albuquerque; Gomes, David Menotti
    Neste artigo, é apresentada uma metodologia automática para a validação fotométrica em sistemas de iluminação interna veicular. Nessa metodologia, propõe-se um método para extração de descritores de homogeneidade de cada região de avaliação. A percepção visual humana, representada pela avaliação do usuário, é usada para classificar as regiões em homogêneas e não-homogêneas. Dois algoritmos de aprendizado de máquina (Redes neurais e Support Vector Machine) são usados para a classificação de regiões visando identificar quais as melhores configurações de descritores irá representar a percepção do usuário em relação à homogeneidade da iluminação dos sistemas de interação com o motorista. Resultados experimentais mostram que a metodologia proposta consegue diferenciar regiões homogêneas de não-homogêneas com precisão superior á 90%.
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    A novel hybrid method for the segmentation of the coronary artery tree in 2D angiograms.
    (2013) Lara, Daniel da Silva Diogo; Faria, Alexandre Wagner Chagas; Araújo, Arnaldo de Albuquerque; Gomes, David Menotti
    Nowadays, medical diagnostics using images have considerable importance in many areas of medicine. Specifically, diagnoses of cardiac arteries can be performed by means of digital images. Usually, this diagnostic is aided by computational tools. Generally, automated tools designed to aid in coronary heart diseases diagnosis require the coronary artery tree segmentation. This work presents a method for a semiautomatic segmentation of the coronary artery tree in 2D angiograms. In other to achieve that, a hybrid algorithm based on region growing and differential geometry is proposed. For the validation of our proposal, some objective and quantitative metrics are defined allowing us to compare our method with another one proposed in the literature. From the experiments, we observe that, in average, the proposed method here identifies about 90% of the coronary artery tree while the method proposed by Schrijver & Slump (2002) identifies about 80%.