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, which increases its diversity and representativeness due to the varying image and bridge properties, such as the orientation of the construction, landscape context (river or Read more
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, digital forensics and biometrics with focus on detection, classification, and spatio-temporal feature encoding. Some relevant work in this topic are: A. Ferreira, L. Bondi, L. Read more
Deep Semantic Segmentation of Mammographic Images MIAS and INbreast are mammographic datasets for the detection and diagnosis of breast cancer. With the dawn of digital mammograms, one important preprocessing step for the tasks of detection and diagnosis is the removal of the pectoral muscle and background area from the images, therefore Read more
[In Portuguese] As políticas de proteção ambiental e climática requerem complexas estruturas de governança ambiental. A ação estatal na área ambiental é implementada por órgãos públicos, instituições e empresas do setor privado. Conflitos de objetivos entre crescimento econômico, proteção ambiental e desenvolvimento social prejudicam uma implementação efetiva e eficiente das Read more
Medical Imaging has been widely used for the diagnosis and detection of several illnesses for decades, as it can capture potentially important characteristics of the body in a non-invasive manner. The advent of Digital Image Processing techniques and afterwards of Machine Learning methods in the area was another important milestone Read more
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.
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.
Land-cover maps are one of the main sources of information for studies that support the creation of public policies in areas like urban planning and environmental monitoring. Their automatic creation involves learning to annotate all samples of a Remote Sensing Image (RSI) from just a few annotated by the user. Read more
Geographical Mapping of Coffee Crops Although new advances in machine learning have revolutionized Computer Vision in recent years for many applications, existing solutions have several limitations when directly applied for agricultural mapping. A first challenge is that automatic mapping using remote sensing images as a data source is generally modeled Read more
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, and clustering purposes. Thus, our research very often includes development and adaptation of supervised and unsupervised learning approaches, typically based on Neural Networks, Evolutionary Algorithms, Read more