Gabriele Pieri

Brain volumes characterisation using hierarchical neural networks

Objective knowledge of tissue density distribution in CT/MRI brain datasets can be related to anatomical or neuro-functional regions for assessing pathologic conditions characterised by slight differences. The process of monitoring illness and its treatment could be then improved by a suitable detection of these variations. In this paper, we present an approach for three-dimensional (3D) classification of brain tissue densities based on a hierarchical artificial neural network (ANN) able to classify the single voxels of the examined datasets. The method developed was tested on case studies selected by an expert neuro-radiologist and consisting of both normal and pathological conditions. The results obtained were submitted for validation to a group of physicians and they judged the system to be really effective in practical applications.

Quantification of Epicardial Fat by Cardiac CT Imaging

The aim of this work is to introduce and design image processing methods for the quantitative analysis of epicardial fat by using cardiac CT imaging. Indeed, epicardial fat has recently been shown to correlate with cardiovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns still remain about the methods for measuring epicardial fat, its regional distribution on the myocardium and the accuracy and reproducibility of the measurements. In this paper, a method is proposed for the analysis of single-frame 3D images obtained by the standard acquisition protocol used for coronary calcium scoring. In the design of the method, much attention has been payed to the minimization of user intervention and to reproducibility issues. In particular, the proposed method features a two step segmentation algorithm suitable for the analysis of epicardial fat. In the first step of the algorithm, an analysis of epicardial fat intensity distribution is carried out in order to define suitable thresholds for a first rough segmentation. In the second step, a variational formulation of level set methods - including a specially-designed region homogeneity energy based on Gaussian mixture models- is used to recover spatial coherence and smoothness of fat depots. Experimental results show that the introduced method may be efficiently used for the quantification of epicardial fat.

Thesaurus project: design of new autonomous underwater vehicles for documentation and protection of underwater archaeological sites

The Thesaurus Project, funded by the Regione Toscana, combines humanistic and technological research aiming at developing a new generation of cooperating Autonomous Underwater Vehicles and at documenting ancient and modern Tuscany shipwrecks. Technological research will allow performing an archaeological exploration mission through the use of a swarm of autonomous, smart and self-organizing underwater vehicles. Using acoustic communications, these vehicles will be able to exchange each other data related to the state of the exploration and then to adapt their behavior to improve the survey. The archival research and archaeological survey aim at collecting all reports related to the underwater evidences and the events of sinking occurred in the sea of Tuscany. The collected data will be organized in a specific database suitably modeled.

Visual sensor networks for infomobility

The wide availability of embedded sensor platforms and low-cost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multi-node processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Active video-surveillance based on stereo and infrared imaging

Video surveillance is a very actual and critical issue at the present time. Within this topics, we address the problem of firstly identifying moving people in a scene through motion detection techniques, and subsequently categorising them in order to identify humans for tracking their movements. The use of stereo cameras, coupled with infrared vision, allows to apply this technique to images acquired through different and variable conditions, and allows an a priori filtering based on the characteristics of such images to give evidence to objects emitting a higher radiance (i.e., higher temperature).

Real time image analysis for infomobility

In our society, the increasing number of information sources is still to be fully exploited for a global improvement in urban living. Among these, a big role is played by images and multimedia data (i.e. coming from CCTV and surveillance videos, traffic cameras, etc.). This along with the wide availability of embedded sensor platforms and low-cost cameras makes it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the characteristics of image processing algorithms coupled to visual sensor networks. In particular the aim is to define strategies to accomplish the tasks of image processing and analysis over these systems which have rather strong constraints in computational power and data transmission. Thus, such embedded platform cannot use advanced computer vision and pattern recognition methods, which are power consuming, on the other hand, the platform may be able to exploit a multi-node strategy that allows to perform a hierarchical processing, in order to decompose a complex task into simpler problems. In order to apply and test the described methods, a solution to a visual sensor network for infomobility is proposed. The experimental setting considered is two-fold: acquisition and integration of different views of parking lots, and acquisition and processing of traffic-flow images, in order to provide a complete description of a parking scenario and its surrounding area.

Computer Vision on Embedded Sensors for Traffic Flow Monitoring

Capillary monitoring of traffic in urban environment is key to a more sustainable mobility in smart cities. In this context, the use of low cost technologies is mandatory to avoid scalability issues that would prevent the adoption of monitoring solutions at the full city scale. In this paper, we introduce a low power and low cost sensor equipped with embedded vision logics that can be used for building Smart Camera Networks (SCN) for applications in Intelligent Transportation System (ITS), in particular, we describe an ad hoc computer vision algorithm for estimation of traffic flow and discuss the findings obtained through an actual field test.

A methodological approach for combining super-resolution and pattern-recognition to image identification

Image acquisition systems integrated with laboratory automation produce multi-dimensional datasets. An effective computational approach for automatic analysis of image datasets is given by pattern recognition methods; in some cases, it can be advantageous to accomplish pattern recognition with image super-resolution procedures. In this paper, we define a method derived from pattern recognition techniques for the recognition of artefacts and noise on set of images combined with super resolution algorithms. The advantage of our approach is automatic artefacts recognition, opening the possibility to build a general framework for artefact recognition independently by the specific application where it is used.

A multilevel neural network model for density volumes classification

The accurate detection of tissue density variation in CT/MRI brain datasets can be useful for analysing and monitoring pathologies with slight differences. In fact, the objective knowledge of density distribution can be related to anatomical structures and therefore the process of monitoring illness and its treatment can be improved. In this paper, we present an approach for the classification of tissue density in three dimensional brain tomographic scans. The proposed approach is based on a hierarchical neural network model able to classify the single voxels of the examined datasets. The approach has been evaluated on both normal and pathological cases selected by an expert neuroradiologist as study cases. The results have shown that the method has a good effectiveness in practical applications and that it can be used for designing a full 3D instrument suitable for supporting the analysis of disease diagnosis and follow-up.

Environmental Monitoring Integrated with a Proactive Marine Information System

In the framework of environmental monitoring, the remote detection and monitoring of oil spills at sea is an important ability due to the high demand of oil based products. This situation causes, that shipping routes are very crowded and the likelihood of oil slicks occurring is also increasing. In this paper we propose a fully integrated and inter-operable information system which can act as a valuable monitoring tool. Such a marine information system is able to monitor ship traffic and marine operators by integrating heterogeneous signals and data through sensing capabilities from a variety of electronic sensors, along with geo-positioning tools, and through a communication infrastructure. This system is able to transfer the integrated data, freely and seamlessly, between different elements of the system itself (and their users). The system also provides a set of decision support services capable of performing functionalities which can act as a support for decision makers.