Classification of color textures with random field models and neural networks

The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial

In this Chapter, a combined approach for classification and localization of simultaneously occurring meeting room acoustic events is presented. For classification,

No obstante, como esta enfermedad afecta a cada persona de manera diferente, no todas las opciones de cuidado y tratamiento pueden ser apropiadas para cada individuo.. La forma

• Difference: The network is removed from all the ensem- bles where it is present, and the performance of such ensembles with the network removed is measured. The value of

We derive the conclusion that together with reliably extracted typical minutiae features, the presence of rare minutiae features helps to improve the measure of confidence of

If we start from a basically homogeneous data set that is not rotationally aligned, then the classification will group images according to their relative orientation (examples 1 and

We tested with very different visual features (GIST, SIFT, Color Histograms, Texture, Concatenations...) and different classifiers. The results were not as expected and we ended in

We study the emerging collective dynamics of a neural network model that emits and recognizes neural signatures with different network topolo- gies in order to assess the capacity of