Encontrados 5 documentos, a visualizar página 1 de 1
Computer Techniques Towards the Automatic Characterization of Graphite Particle...
João P. Papa; Rodrigo Y. M. Nakamura; Victor Hugo C. de Albuquerque; Alexandre X. Falcão; João Manuel R. S. TavaresThe automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the...
Efficient Supervised Optimum-Path Forest Classification for Large Datasets
João P. Papa; Victor Hugo C. de Albuquerque; Alexandre X. Falcão; João Manuel R. S. TavaresToday data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without losing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the training samples. ...
Speeding Up Optimum-Path Forest Training by Path-cost Propagation
Adriana S. Iwashita; João P. Papa; Alexandre X. Falcão; Roberto A. Lotufo; Victor M. de Araujo; Victor H. Costa de AlbuquerqueIn this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training.
Precipitates Segmentation from Scanning Electron Microscope Images through Mach...
João P. Papa; Clayton R. Pereira; Victor H.C. de Albuquerque; Cleiton C. Silva; Alexandre X. Falcão; João Manuel R. S.TavaresThe presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning ...
Application of optimum-path forest classifier for synthetic material porosity s...
Victor H. C. Albuquerque; João P. Papa; Alexandre X. Falcão; Pedro P. R. Filho; João Manuel Ribeiro da Silva TavaresThis paper presents a new application and evaluation of the Optimum-Path Forest (OPF) classifier to accomplish synthetic material porosity segmentation and quantification obtained from optical microscopic images. Sample images of a synthetic material were analyzed and the quality of the results was confirmed by human visual analysis. Additionally, the OPF results were compared against two different Support Vect...
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