Publications

Wide open areas represent challenging scenarios forsurveillance systems, since sensory data can be affected bynoise, uncertainty, and distractors. Therefore, the tasks oflocalizing and identifying targets (e.g., people) in such environmentssuggest to go beyond the use of camera-only deployments.In this paper, we propose an innovative systemrelying on the joint use of cameras and RFIDs, allowing usto “map” RFID tags to people detected by cameras and,thus, highlighting potential intruders. To this end, sophisticatedfiltering techniques preserve the uncertainty of dataand overcome the heterogeneity of sensors, while an evidentialfusion architecture, based on Transferable Belief Model,combines the two sources of information and manages conflictbetween them. The conducted experimental evaluationshows very promising results.

A new evidential fusion architecture is proposed to build anhybrid articial intelligent system for people surveillance in wide open areas. Authorized people and intruders are identied and localized thanks to the joint employment of cameras and RFID tags. Complex Event Processing and Transferable Belief Model are exploited for handling noisy data and uncertainty propagation. Experimental results on complex synthetic scenarios demonstrate the accuracy of the proposed solution.

We propose a formulation of people tracking problem as a Transductive Learning (TL) problem. TL is an effective semi-supervised learning technique by which many classification problems have been recently reinterpreted as learning labels from incomplete datasets. In our proposal the joint exploitation of spectral graph theory and Riemannian manifold learning tools leads to the formulation of a robust approach for appearance based tracking in Video Surveillance scenarios. The key advantage of the presented method is a continuously updated model of the tracked target, used in the TL process, that allows to on-line learn the target visual appearance and consequently to improve the tracker accuracy. Experiments on public datasets show an encouraging advancement over alternative state-of the-art techniques.

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.

The artistic content of historical manuscripts provides a lot of challenges in terms of automatic text extraction, picture segmentation and retrieval by similarity. In particular this work addresses the problem of automatic extraction of meaningful pictures, distinguishing them from handwritten text and floral and abstract decorations. The proposed solution firstly employs a circular statistics description of a directional histogram in order to extract text. Then visual descriptors are computed over the pictorial regions of the page: the semantic content is distinguished from the decorative parts using color histograms and a novel texture feature called Gradient Spatial Dependency Matrix. The feature vectors are finally processed using an embedding procedure which allows increased performance in later SVM classification. Results for both feature extraction and embedding based classification are reported, supporting the effectiveness of the proposal on high resolution replicas of artistic manuscripts.

In this paper we present a methodology to evaluate the binding free energy of a miRNA-mRNA complex through Molecular Dynamics-Thermodynamic Integration simulations. We applied our method on the C-elegans let-7 miRNA:lin-41 mRNA complex, known to be a validate miRNA:mRNA interaction, in order to evaluate the energetic stability of the structure. The methodology has been designed to face the various challenges of nucleic acid simulations and binding free energy computations and to allow an optimal trade-off between accuracy and computational cost.

In computer science, contextual information can be used both to reduce computations and to increase accuracy. This paper discusses how it can be exploited for people surveillance in very cluttered environments in terms of perspective (i.e., weak scenecalibration) and appearance of the objects of interest (i.e., relevance feedback on the training of a classifier). These techniques are applied to a pedestrian detector that uses a LogitBoost classifier, appropriately modified to work with covariance descriptors which lie on Riemannian manifolds. On each detected pedestrian, a similar classifier is employed to obtain a precise localization of the head. Two novelties on the algorithms are proposed in this case: polar image transformations to better exploit the circular feature of the head appearance and multispectral image derivatives that catch not only luminance but also chrominance variations. The complete approach has been tested on the surveillance of a construction site to detect workers that do not wear the hard hat: in such scenarios, the complexity and dynamics are very high, making pedestrian detection a real challenge.

Video surveillance is becoming the technology of choice for monitoring crowded areas for security threats. While video provides ample information for human inspectors, there is a great need for robust automated techniques that can efficiently detect anomalous behavior in streaming video from single ormultiple cameras. In this work we synergistically combine two state-of-the-art methodologies. The rst is the ability to track and label single person trajectories in a crowded area using multiple video cameras, and the second is a new class of novelty detection algorithms based on spectral analysis of graphs. By representing the trajectories as sequences of transitions betweennodes in a graph, shared individual trajectories capture only a small subspace of the possible trajectories on the graph. This subspace is characterized by large connected components of the graph, which are spanned by the eigenvectors with the low eigenvalues of the graph Laplacian matrix. Using this technique, we develop robust invariant distance measures for detectinganomalous trajectories, and demonstrate their application on realvideo data.

Embedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embeddedsmart camera by hardware-level operations when performingobject detection and tracking. The CITRIC platform is employedas our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the imagesensor rather than performing software-level down-sampling atthe main microprocessor of the camera board. In addition, instead of performing object detection and tracking on wholeimage, we first estimate the location of the target in the nextframe, form a search region around it, then crop the next frameby using the HREF and VSYNC signals at the micro-controllerof the image sensor, and perform detection and tracking onlyin the cropped search region. Thus, the amount of data thatis moved from the image sensor to the main memory at eachframe is optimized. Also, we can adaptively change the size ofthe cropped window during tracking depending on the objectsize. Reducing the amount of transferred data, better use ofthe memory resources, and delegating image down-samplingand cropping tasks to the micro-controller on the image sensor,result in significant decrease in energy consumption and increasein battery-life. Experimental results show that hardware-leveldown-sampling and cropping, and performing detection andtracking in cropped regions provide 41.24% decrease in energyconsumption, and 107.2% increase in battery-life. Compared toperforming software-level down-sampling and processing wholeframes, proposed methodology provides an additional 8 hours ofcontinuous processing on 4 AA batteries, increasing the lifetimeof the camera to 15.5 hours.