Publications

Long term tracking of people in unconstrained scenarios is still an open problem due to the absence of constant elements in the problem setting. The camera, when active, may move and both the background and the target appearance may change abruptly leading to the inadequacy of most standard tracking techniques. We propose to exploit a learning approach that considers the tracking task as a semi supervised learning (SSL) problem. Given few target samples the aim is to search the target occurrences in the video stream re-interpreting the problem as label propagation on a similarity graph. We propose a solution based on graph transduction that works iteratively frame by frame. Additionally, in order to avoid drifting, we introduce an update strategy based on an evolutionary clustering technique that chooses the visual templates that better describe target appearance evolving the model during the processing of the video. Since we model people appearance by means of covariance matrices on color and gradient information our framework is directly related to structure learning on Riemannian manifolds. Tests on publicly available datasets and comparisons with stateof- the-art techniques allow to conclude that our solution exhibit interesting performances in terms of tracking precision and recall in most of the considered scenarios.

The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark.

We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots. Experiments are performed to demonstrate the effectiveness of our approach by comparing our algorithm against recent proposals for automatic scene segmentation. We also propose an improved performance measure that aims to reduce the gap between numerical evaluation and expected results, and propose and release a new benchmark dataset.

Smart environments are now designed as natural interfaces to capture and understand human behavior without a need for explicit human-computer interaction. In this paper, we present a general-purpose architecture that acquires and understands human behaviors through a sensing floor. The pressure field generated by moving people is captured and analyzed. Specific actions and events are then detected by a low-level processing engine and sent to high-level interfaces providing different functions. The proposed architecture and sensors are modular, general-purpose, cheap, and suitable for both small- and large-area coverage. Some sample entertainment and virtual reality applications that we developed to test the platform are presented.

Although anaplastic large-cell lymphomas (ALCL) carrying anaplastic lymphoma kinase (ALK) have a relatively good prognosis, aggressive forms exist. We have identified a novel translocation, causing the fusion of the TRAF1 and ALK genes, in one patient who presented with a leukemic ALK+ ALCL (ALCL-11). To uncover the mechanisms leading to high-grade ALCL, we developed a human patient-derived tumorgraft (hPDT) line. Molecular characterization of primary and PDT cells demonstrated the activation of ALK and nuclear factor kB (NFkB) pathways. Genomic studies of ALCL-11 showed the TP53 loss and the in vivo subclonal expansion of lymphoma cells, lacking PRDM1/Blimp1 and carrying c-MYC gene amplification. The treatment with proteasome inhibitors of TRAF1-ALK cells led to the downregulation of p50/p52 and lymphoma growth inhibition. Moreover, a NFkB gene set classifier stratified ALCL in distinct subsets with different clinical outcome. Although a selective ALK inhibitor (CEP28122) resulted in a significant clinical response of hPDT mice, nevertheless the disease could not be eradicated. These data indicate that the activation of NFkB signaling contributes to the neoplastic phenotype of TRAF1-ALK ALCL. ALCL hPDTs are invaluable tools to validate the role of druggable molecules, predict therapeutic responses and implement patient specific therapies.

Multimedia forensics is a new emerging discipline regarding the analysis and exploitation of digital data as support for investigation to extract probative elements. Among them, visual data about people and people activities, extracted from videos in an efficient way, are becoming day by day more appealing for forensics, due to the availability of large video-surveillance footage. Thus, many research studies and prototypes investigate the analysis of soft biometrics data, such as people appearance and people trajectories. In this work, we propose new solutions for querying and retrieving visual data in an interactive and active fashion for soft biometrics in forensics. The innovative proposal joins the capability of transductive learning for semi-supervised search by similarity and a typical multimedia methodology based on user-guided relevance feedback to allow an active interaction with the visual data of people, appearance and trajectory in large surveillance areas. Approaches proposed are very general and can be exploited independently by the surveillance setting and the type of video analytic tools.

The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging.