Explore projects

Sync Projects maintained only on Github into Gitlab, for operational reasons.

The inputs are texture features extracted from images of honeybee pollen loads captured by a stereo microscope connected to a digital camera. 2.600 instances, 70 inputs, 5 classes (species of plants cytisus, castanea, quercus, rubus and paphanus).

The inputs are shape and texture features extracted from microscopical images of pollen grains in the air. 291 instances, 23 inputs, 3 classes (species of plants Parietaria Judaica, urtica urens and urtica membranacea).

The inputs are bands of infrared hyperspectral imaging data of potatoes. 126 instances, 256 inputs, 2 classes (presence/absence of common scab in potatoes).

Texture features extracted from histological images of fish ovary. Total 4 sets; 3.868 instances. 25 (rep. 41) inputs, 2 (resp. 3) classes to detect the cell nucleous (resp. to classify maturity cell states).

This project contains the material used to carry out the study of the following paper: "On the use of feature selection to improve oil spill detection in SAR images"

Clase de LaTeX para a elaboración de tesis no formato CITIUS.

Experimental results related to the paper "Spectral-Spatial Classification of Hyperspectral Images Based on Convolutional Neural Networks"