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Human detection in digital videos is challenging since the human appearance may widely vary. Several algorithms to detect humans in digital images have been recently developed, such as the Aggregated Chanel Features (ACF). Most of them are based on features related to the shape. These algorithms give the best results regarding accuracy but generate many false alarms. In this paper, we propose to use...
In this paper, it is proposed a neural network based on by AutoAssociative Pyramidal Neural Network and their architecture, which uses concepts of receptive fields and autoassociative memory. These concepts are widely used in models of artificial neural networks and were incorporated into model proposed in this work. Furthermore, the proposed neural network also uses the concept of sharing weights...
This paper aims to evaluate and compare different spatial filtering methods on EEG data in order to improve the classification of Motor Imagery electrical brain information. Multiple spatial filtering methods, such as Laplacian filter, Common Average Reference method, Weighted Average Filter, Spatial Smoothing Filter, Common Spatial Patterns method and the Non-Homogeneous filter, have been analyzed...
Emotional processing of ex-combatants is affected by chronic exposure to violent events. For a successful reintegration into society, it is necessary to discriminate their brain responses from civilian people, as a first stage to develop treatment strategies. This paper presents a comparative analysis between a Multilayer Perceptron Neural Network and a Fuzzy C-Means classifier to differentiate ex-combatant...
Brain Computer Interfaces allow the interaction between a person and their environment using signals extracted directly from the brain. One of the most common non-invasive methods of brain signal acquisition is the electroencephalography (EEG). An EEG based BCI system requires the processing and translation of the EEG signal into significant features that could be converted into commands for the external...
Selecting an adequate machine learning model, e.g. for feature selection or classification, is a very important task in developing machine learning applications. In order to perform an adequate selection, statistic tests are introduced by several approaches but some of them are hard to reproduce in different case studies due to the lack of a systematic application procedure. This work presents a methodological...
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