In this work, we propose a novel approach to detect and track, in videoconference sequences, six landmarks on eyes: the four corners and the pupils. Detection is based on the Inner Product Detector (IPD), and tracking on the Lucas-Kanade (LK) technique. The novelty of our method consists in the integration between detection and tracking, the evaluation of the temporal consistency to decrease the false positive rates, and the use of geometrical constraints to infer the position of missing points. In our experiments, we use five high definition video sequences with four subjects, different types of background, fast movements, blurring and occlusion. The obtained results have shown that the proposed technique is capable of detecting and tracking landmarks with good reliability.
This work proposes an automatic fault classifier that uses similarity-based modeling (SBM) to identify faults on rotating machines. The similarity model can be used either as an auxiliary model to generate features for a classifier or as a standalone classifier. A new approach for training the model using a prototype-selection method is investigated. Experimental results are shown for the MaFaulDa database and for the Case Western Reserve University (CWRU) bearing database. Results indicate that the proposed modifications improve the generalization power of the similarity model and of the associated classifier, achieving accuracies of 96.4% on the MaFaulDa and 98.7% on the CWRU databases.
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