Publications

RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data

Protein networks are usually used to describe the interacting behaviours of complex biosystems. Bioinformatics must be able to provide methods to mine protein undirected networks and to infer subnetworks of interacting proteins for identifying relevant biological pathways. Here we present FunMod an innovative Cytoscape version 2.8 plugin able to identify biologically significant sub-networks within informative protein networks, enabling new opportunities for elucidating pathways involved in diseases. Moreover FunMod calculates three topological coefficients for each subnetwork, for a better understanding of the cooperative interactions between proteins and discriminating the role played by each protein within a functional module. FunMod is the first Cytoscape plugin with the ability of combining pathways and topological analysis allowing the identification of the key proteins within sub-network functional modules.

In this paper, we propose a general approach for automatic segmentation, color-based retrieval and classification of garments in fashion store databases, exploiting shape and color information. The garment segmentation is automatically initialized by learning geometric constraints and shape cues, then it is performed by modeling both skin and accessory colors with Gaussian Mixture Models. For color similarity retrieval and classification, to adapt the color description to the users’ perception and the company marketing directives, a color histogram with an optimized binning strategy, learned on the given color classes, is introduced and combined with HOG features for garment classification. Experiments validating the proposed strategy, and a free-to-use dataset publicly available for scientific purposes, are finally detailed.

A measurement device that is extremely important for Unmanned Aerial Vehicle (UAV) guidance and control purposes is the airspeed sensor. As the parameters of feedback control laws are conventionally scheduled as a function of airspeed, an incorrect reading (e.g. due to a sensor fault) of the Pitot-static tube could induce an incorrect feedback control action, potentially leading to the loss of control of the UAV. The objective of this study is to establish the accuracy and reliability of the two airspeed estimation techniques for eventual use as the basis for real-time fault detection of anomalies occurring on the Pitot-static tube sensor. The first approach is based on an Extended Kalman Filter (EKF) and the second approach is based on Least Squares (LS) modeling. The EKF technique utilizes nonlinear kinematic relations between GPS, Inertial Measurement Unit and Air Data System signals and has the advantage of independence from knowledge of the aircraft model. The LS method is based on explicit knowledge of the aircraft model and has the advantage of on-line computation of the airspeed estimate, with minimal computational effort. The performance analysis was carried out with flight data from the WVU YF-22 UAV research platform. The results of the analysis indicate that the two methods provide essentially comparable performance in terms of mean (∼1 m/s) and standard deviation (∼1.5 m/s) of the airspeed estimation error which is about the 5% of the mean in-flight velocity of 32 m/s.

In this paper we propose to extend the well known graph cut segmentation framework by learning superpixel relations and use them to weight superpixel-to-superpixel edges in a superpixel graph. Adjacent superpixel-pairs are analyzed to build an object boundary model, able to discriminate between superpixel-pairs belonging to the same object or placed on the edge between the foreground object and the background. Several superpixel-pair features are investigated and exploited to build a non-linear SVM to learn object boundary appearance. The adoption of this modified graph cut enhances the performance of a previously proposed segmentation method on two publicly available datasets, reaching state-of-the-art results.

Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

Museum visitor experiences differ from person to person, from cognitive to affective experiences. Progress in information technology has provided us with the opportunity to improve both the quantity and personalization of cultural information, privileging the cognitive experience against the affective. Computer vision promises to be an extraordinary enabling technology for augmenting visitor experiences, bridging the affective gap by understanding the visitor's individual cognitive needs and interests and his or her situational affective state. © 2014 IEEE.