Miguel Tavares Coimbra

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In this paper, we investigate the applicability of Electrocardiogram (ECG) signals for human identification. Wavelet Transform (WT) and Independent Component Analysis (ICA) methods are applied to extract morphological features that appear to offer excellent discrimination among subjects. The proposed method is aimed at the two-lead ECG configuration that is(More)
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two(More)
—MPEG-2 compressed domain information, namely motion vectors and DCT coefficients, is filtered and manipulated to obtain a motion field using a two-dimensional (2-D) trans-lational model. The results are compared to a popular optical flow method, more specifically the one presented by Lucas and Kanade, revealing very good results. Our method provides a very(More)
Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and(More)
Wearable health monitoring devices have been widely explored to enable continuous monitoring of physiological vital signals, such as electrocardiogram (ECG). In this work, we investigate the applicability of ECG signals from such wearable devices in human identification. In the 5-subject study we undertook, the proposed method exhibits near-100% recognition(More)
Endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper proposes and compares two approaches (Bayesian and support vector machines) that can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic(More)
The endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper shows how support vector machines can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric(More)
In this paper, we consider the problem of separating a set of independent components when only one movable sensor is available to record the mixtures. We propose to exploit the quasi-periodicity of the heart signals to transform the signal from this one moving sensor, into a set of measurements, as if from a virtual array of sensors. We then use ICA to(More)