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This paper address the dust aerosol detection problem based on a probabilistic multispectral image analysis. Two classifiers are designed. First the Maximum Likelihood classifier is adapted to mode different types of atmospheric components. The second is a Probabilistic Neural Network (PNN) model. The data sets are MODIS multispectral bands from NASA Terra satellite. Findings indicate that the PNN...
Nowadays human beings need to perform dangerous tasks involving the possibility of physical injury. This creates a demand of novel collaborative technologies between human and machines. In this paper we present a collaborative scheme aimed to support a mobile face recognition system for surveillance applications. Such face recognition system is based on facial feature lines and the Abonyi's and Szeifert's...
Quantitative colocalization analysis in fluorescent microscopy imaging is a promising procedure used to perform functional protein analysis. Images acquired are degraded, and the features extracted are affected by this degradation. Moreover, the classification of the data becomes uncertain. In this paper, we address an application of SOM to a clustering problem formulated via feature extraction from...
This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the feature vector incorporates eigenvectors of...
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