Publications

Several local features have become quite popular for concept detection and search, due to their ability to capture distinctive details. Typically a Bag of Words approach is followed, where a codebook is built by quantizing the local features. In this paper, we propose to represent SIFT local features extracted from an image as a multivariate Gaussian distribution, obtaining a mean vector and a covariance matrix. Differently from common techniques based on the Bag of Words model, our solution does not rely on the construction of a visual vocabulary, thus removing the dependence of the image descriptors on the specific dataset and allowing to immediately retargeting the features to different classification and search problems. Experimental results are conducted on two very different Cultural Heritage image archives, composed of illuminated manuscript miniatures, and architectural elements pictures collected from the web, on which the proposed approach outperforms the Bag of Words technique both in classification and retrieval.

nti-nuclear antibodies test is based on the visual evaluation of the intensity and staining pattern in HEp-2 cell slides by means of indirect immunofluorescence (IIF) imaging, revealing the presence of autoantibodies responsible for important immune pathologies. In particular, the categorization of the staining pattern is crucial for differential diagnosis, because it provides information about autoantibodies type. Their manual classification is very time-consuming and not very reliable, since it depends on the subjectivity and on the experience of the specialist. This motivates the growing demand for computer-aided solutions able to perform staining pattern classification in a fully automated way. In this work we compare two classification techniques, based respectively on Support Vector Machines and Subclass Discriminant Analysis. A set of textural features characterizing the available samples are first extracted. Then, a feature selection scheme is applied in order to produce different datasets, containing a limited number of image attributes that are best suited to the classification purpose. Experiments on IIF images showed that our computer-aided method is able to identify staining patterns with an average accuracy of about 91% and demonstrate, in this specific problem, a better performance of Subclass Discriminant Analysis with respect to Support Vector Machines.

Portable devices for first-person camera views will play a central role in future interactive systems. One necessary step for feasible human-computer guided activities is gesture recognition, preceded by a reliable hand segmentation from egocentric vision. In this work we provide a novel hand segmentation algorithm based on Random Forest superpixel classification that integrates light, time and space consistency. We also propose a gesture recognition method based Exemplar SVMs since it requires a only small set of positive samples, hence it is well suitable for the egocentric video applications. Furthermore, this method is enhanced by using segmented images instead of full frames during test phase. Experimental results show that our hand segmentation algorithm outperforms the state-of-the-art approaches and improves the gesture recognition accuracy on both the publicly available EDSH dataset and our dataset designed for cultural heritage applications.

The research on innovative and natural interfaces aims at developing devices able to capture and understand the human behavior without the need of a direct interaction. In this paper we propose and describe a framework based on a sensing floor device. The pressure field generated by people or objects standing on the floor is captured and analyzed. Local and global features are computed by a low level processing unit and sent to high level interfaces. The framework can be used in different applications, such as entertainment, education or surveillance. A detailed description of the sensing element and the processing architectures is provided, together with some sample applications developed to test the device capabilities.

Techniques based on Bag Of Words approach represent images by quantizing local descriptors and summarizing their distribution in a histogram. Dierently, in this paper we describe an image as multivariate Gaussian distribution, estimated over the extracted local descriptors. The estimated distribution is mapped to a high-dimensional descriptor, by concatenating the mean vector and the projection of the covariance matrix on the Euclidean space tangent to the Riemannian manifold. To deal with large scale datasets and high dimensional feature spaces the Stochastic Gradient Descent solver is adopted. The experimental results on Caltech-101 and ImageCLEF2011 show that the method obtains competitive performance with state-of-the art approaches.

Nowadays, intelligent video surveillance has become an essential tool of the greatest importance for several security-related applications. With the growth of installed cameras and the increasing complexity of required algorithms, in-house self-contained video surveillance systems become a chimera for most institutions and (small) companies. The paradigm of Video Surveillance as a Service (VSaaS) helps distributing not only storage space in the cloud (necessary for handling large amounts of video data), but also infrastructures and computational power. This chapter will briefly introduce the motivations and the main characteristics of a VSaaS system, providing a case study where research-lab computer vision algorithms are integrated in a VSaaS platform. The lessons learnt and some future directions on this topic will be also highlighted.

People re-identification is a challenging problem in surveillance and forensics and it aims at associating multiple instances of the same person which have been acquired from different points of view and after a temporal gap. Image-based appearance features are usually adopted but, in addition to their intrinsically low discriminability, they are subject to perspective and view-point issues. We propose to completely change the approach by mapping local descriptors extracted from RGB-D sensors on a 3D body model for creating a view-independent signature. An original bone-wise color descriptor is generated and reduced with PCA to compute the person signature. The virtual bone set used to map appearance features is learned using a recursive splitting approach. Finally, people matching for re-identification is performed using the Relaxed Pairwise Metric Learning, which simultaneously provides feature reduction and weighting. Experiments on a specific dataset created with the Microsoft Kinect sensor and the OpenNi libraries prove the advantages of the proposed technique with respect to state of the art methods based on 2D or non-articulated 3D body models.