Research Programs

Signal Processing and Recognition

The SPR Lab aims at developing research activities revolving around the design of smart computerized signal/image processing and recognition systems.

Research Areas

Image and signal processing methodologies for dealing with mono-, multi- and hyper-dimensional signals/images as well as multimodal signals/images. The main faced issues are adaptive filtering, segmentation and reconstruction of missing data.

Pattern recognition and machine learning approaches to cope with classification, regression and prediction problems in various application domains. Developed technologies are based for instance on support vector machines, artificial neural networks, kernel methods, statistical reasoning, data fusion as well as evolutionary computation and swarm intelligence.

Computer vision technologies to retrieve information from image or video data. They include 2D/3D detectors, descriptors, trackers, object/people recognition and scene understanding.

Advanced negotiation and interaction mechanisms to support social environments through autonomous facilitators (software agents) capable of discovering and learning humans’ behaviors. Data mining and collaborative filtering techniques are used by agents to discover no trivial relations among actions of different users and support their activities within the social environment (Implicit Culture framework).

Data, content and knowledge representation technologies capable to overcome scalability issues characterizing the existing approaches. The key idea is to view diversity as a valuable feature which must be maintained and exploited through a bottom-up strategy and not as a useless feature that must be absorbed in some general schema.

Data Mining through techniques for classification and causal relationship discovery. The main problems addressed are those of increasing the performance for applications to big datasets. The developed classification techniques exploit local models achieving fast and scalable local versions of support vector machines. Moreover, subsampling and resampling are used to increase the effectiveness of causal discovery.

Application Areas

The continuous enhancement of remote sensors mounted on satellites, aircraft or unmanned aerial vehicles (UAV) has increased the importance of remote sensing in key real-world applications such as environmental monitoring (mapping, agriculture, precision farming, urban monitoring, forestry, inland and outland waters, and disaster prevention and monitoring). In particular, one of the reasons for the growing interest of numerous private and governmental end-users in the exploitation of remote-sensing data is represented by the higher quality and larger quantity of information that can be extracted through the analysis of remote-sensing images acquired over a given geographical area and characterized by improved spatial and spectral resolutions and reduced revisit time. SPR is particularly active in the development of technologies capable to better exploit such data and to follow the technological advances of remote sensors.

The automatic analysis of electrocardiogram (ECG) signals has received great attention from the biomedical engineering community since ECG provides cardiologists with useful information about the rhythm and functioning of the heart. SPR has been focusing considerable efforts to design innovative systems for monitoring patients suffering from arrhythmia pathologies and prenatal cardiac activity.

Recently, quantitative applications of infrared spectroscopy in various chemical fields including pharmaceutical, food and textile industries have grown drastically. Indeed, chemical analysis by spectrophotometry is very promising since it relies on a fast acquisition of a large number (hundreds and even thousands) of spectral data, which if suitably exploited can yield accurate estimations of the concentration of a chemical component of interest in a given product. I2P is developing advanced regression technologies for automating the quality control in food industries.

According to a recent INPS census, more than 120.000 visually impaired people live in Italy. Thus, any technology that can assist visually impaired people in general and blind people in particular may have a very significant social impact. SPR is actively involved in the design of compact and smart assistive technologies aiming at recovering partially the sight to blind people in indoor public and private environments, not only by helping them in moving autonomously but also in recognizing objects and people in such environments.

The Implicit Culture framework has been applied for the development of knowledge management systems, the design and the run-time execution of service-oriented systems and in social environments where human users can interact through their mobile devices.



Faculty Members

Giulia BoatoNicola ConciFarid Melgani (Coord)

Further Information

Technical reports of Research Program
Published papers of Research Program