Caio Da Silva

Bridge Dataset

Bridge Dataset This dataset is composed of 500 images each containing at least one bridge. This dataset has samples collected from different regions around the world,… Read More »Bridge Dataset

Computer vision

Our team is involved in the research collaborations that involve many classical Computer Vision applications. Our actuation mainly concern image and video processing for surveillance,… Read More »Computer vision

Brazilian Cerrado-Savanna Scenes Dataset

The dataset is composed of 1,311 multi-spectral scenes extracted from images acquired by the RapidEye satellite sensors over the Serra do Cipó region, a mountainous and highly biodiverse and heterogenous landscape in southern-central Brazil mainly constituted of Cerrado-Savanna Vegetation.
From the 5 bands (blue, green, red, red edge and near infrared) that the images acquired by the RapidEye satellite sensors have, we have selected three (near-infrared, green, and red bands), which are the most useful and representative ones for discriminating vegetation areas.
It is a very challenging dataset given its high intraclass variance, caused by different spatial configurations and densities of the same vegetation type, as well as its high interclass similarity, given similar appearance of different types of vegetation species.

Brazilian Coffee Scenes Dataset

This dataset is a composition of scenes taken by SPOT sensor in 2005 over four counties in the State of Minas Gerais, Brazil: Arceburgo, Guaranesia, Guaxupé and Monte Santo. It has many intraclass variance caused by different crop management techniques. Also, coffee is an evergreen culture and the South of Minas Gerais is a mountainous region, which means that this dataset includes scenes with different plant ages and/or with spectral distortions caused by shadows.

Machine Learning

Our Laboratory is interested in the study and development of machine learning techniques mainly for classification, feature learning, active learning, temporal series analysis, feature selection,… Read More »Machine Learning