Publications

The discovery by Morris et al. (1994) of the genes contributing to the t(2;5)(p23;q35) translocation has laid the foundation for a molecular based recognition of anaplastic large cell lymphoma and highlighted the need for a further stratification of T-cell neoplasia. Likewise the detection of anaplastic lymphoma kinase (ALK) genetic lesions among many human cancers has defined unique subsets of cancer patients, providing new opportunities for innovative therapeutic interventions. The objective of this review is to appraise the molecular mechanisms driving ALK-mediated transformation, and to maintain the neoplastic phenotype. The understanding of these events will allow the design and implementation of novel tailored strategies for a well-defined subset of cancer patients.

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: the trade-off between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach, since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged in a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades and SVM) and features (covariance matrices, Haar-like features, integral channel features and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.

Image collections are searched in common retrieval systems in many different ways, but the typical presentation is by means of a grid styled view. In this paper we try to suggest a novel use of relevance feedback as a tool to warp the view and allow the user to spatially navigate the image collection, and at the same time focus on his retrieval aim. This is obtained by the use of a distance based space warping on the 2D projection of the distance matrix.

Safety and security reasons are pushing the growth of surveillance systems, for both prevention and forensic tasks. Unfortunately, most of the installed systems have recording capability only, with quality so poor that makes them completely unhelpful. This chapter will introduce the concepts of modern systems for Intelligent Video Surveillance (IVS), with the claim of providing neither a complete treatment nor a technical description of this topic but of representing a simple and concise panorama of the motivations, components, and trends of these systems. Different from CCTV systems, IVS should be able, for instance, to monitor people in public areas and smart homes, to control urban traffi c, and to identity assessment for security and safety of critical infrastructure.

In this paper we propose a color-based approach for skin detection and interest garment selection aimed at an automatic segmentation of pieces of clothing. For both purposes, the color description is extracted by an iterative energy minimization approach and an automatic initialization strategy is proposed by learning geometric constraints and shape cues. Experiments confirms the good performance of this technique both in the context of skin removal and in the context of classification of garments.

Despite the many efforts in finding effective feature sets or accurate classifiers for people detection, few works have addressed ways for reducing the computational burden introducedby the sliding window paradigm. This paper proposes a multi-stage procedure for refining the search for pedestrians using the HOG features and the monolithic SVM classifier. The multi-stage procedure is based on particle-based estimation of pdfs and exploits the margin provided by the classifier to draw more particles on the areas where the classifier’s response is higher. This iterative algorithm achieves the same accuracy than sliding window using less particles (and thus being more efficient) and, conversely, is more accurate when configured to work at thesame computational load. Experimental results on publicly available datasets demonstrate that this method, previouslyproposed for boosted classifiers only, can be successfully applied to monolithic classifiers.

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.

Following people in different video sources is a challenging task: variations in the type of camera, in the lighting conditions, in the scene settings (e.g. crowd or occlusions) and in the point of view must be accounted. In this paper we propose a system based only on appearance information that, disregarding temporal and spatial information, can be flexibly applied on both moving and static cameras. We exploit the joint use of transductive learning and spectral properties of graph Laplacians proposing a formulation of the people tracing problem as a semi-supervised classification. The knowledge encoded in two labeled input sets of positive and negative samples of the target person and the continuous spectral update of these models allow us to obtain a robust approach for people tracing in surveillance video sequences. Experiments on publicly available datasets show satisfactory results and exhibit a good robustness in dealing with short and long term occlusions.