David F. Ramirez-Moreno

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Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were(More)
Itti and Koch’s (Vision Research 40:1489–1506, 2000) saliency-based visual attention model is a broadly accepted model that describes how attention processes are deployed in the visual cortex in a pure bottom-up strategy. This work complements their model by modifying the color feature calculation. Evidence suggests that S-cone responses are elicited in the(More)
The acoustic startle reflex (ASR), a defensive response, is a contraction of the skeletal and facial muscles in response to an abrupt, intense (> 80 db) auditory stimulus, which has been extensively studied in rats and humans. Prepulse inhibition (PPI) of ASR is the normal suppression of the startle reflex when an intense stimulus is preceded by a weak(More)
This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transient responses to moving(More)
This work develops a microcalcifications’ detection system in mammograms by using difference of Gaussians filters (DoG) and artificial neural networks (ANN). The digital image processing proposed show the basic wavelet-based behavior of DoG as a mother function frequently used in several vision tasks, and in this case, used in order to enhance the(More)
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