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We investigate exploiting the class specific information in the conventional perceptual edge grouping for the task of object extraction, since the domain information is usually available in practice. Instead of applying the classical Gestalt principles, we turn to learn a class specific probabilistic structure model from training images. During the learning, both geometrical and photometric features...
Clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. In this paper, the problem of modeling projected clusters is first discussed, and an extended Gaussian model is proposed...
In this paper a new approach for obtaining LOLP curves is presented. It is based on the neural network called multilayer perceptron (MLP). The method is based on the MLP ability to extract, from a sufficiently general training data set, the existing relationships between variables whose interdependence is unknown a priori. The MLP is trained with LOLP data from locations where they are available,...
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