Jorge de la Calleja

Learn More
In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We have used a neural network, and a locally weighted regression method, and also we implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble(More)
We describe an approach to perform content-based retrieval on a database of astronomical images. The method first employs image processing to normalize the images, eliminating the effects of orientation and scale, then it performs principal component analysis to reduce the dimensionality of the data, and finally, retrieval is done using the nearest(More)
In this paper we present an automated method for classifying astronomical objects in multi-spectral wide-field images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object(More)
In this paper we present experimental results of an automated method for image-based classification of diabetic retinopathy. The method is divided into three stages: image processing, feature extraction and image classification. In the first stage we have used two image processing techniques in order to enhance their features. Then, the second stage reduces(More)
The preservation of temporal dependencies among a group of processes that exchange continuous media at runtime is a key issue for emerging mobile distributed systems (MDS), such as monitoring of biosignals and interactive multiuser games. Although several works are oriented to satisfy temporal dependencies, most of them are not suitable for MDSs. In(More)