Publications

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.

In this paper, we suggest an interaction model designed to fit users' expectations in front of an image retrieval system. A lightweight relevance feedback strategy, working directly on the 2D projection of image features, allows the user to spatially navigate the media collection maintaining the real-time constraint. A preliminary evaluation of this relevance feedback strategy shows good performance compared with other known approaches.

Understanding social and collective people behaviour in open spaces is one of the frontier of modern video surveillance. Many sociological theories, and proxemics in particular, have been proved their validity as a support for classifying and interpreting human behaviour. Proxemics suggest some simple but effective behavioural rules, useful to understand what people are doing and their social involvement with other individuals. In this paper we propose to extend the proxemics analysis along the time and provide a solution for analysing sequences of proxemic states computed between trajectories of people pairs (dyads). Trajectories, computed from videosurveillance videos, are first analysed and converted to a sequence of symbols according to proxemic theory. Then an elastic measure for comparing those sequences is introduced. Finally, interactions are classified both in an off-line unsupervised way and in an on-line fashion. Results on videosurveillance data, demonstrate that sequences of proxemic states can be effective in characterizing mutual interactions and experiments in capturing the most frequent dyads interactions and on-line classifying them when a labelled training set is available are proposed.

Multimedia technologies have recently created the conditions for a true revolution in the Cultural Heritage domain, particularly in reference to the study, exploitation, and fruition of artistic works. New opportunities are arising for researchers in the field of multimedia to share their research results with people coming from the field of art and culture, and viceversa. This paper gathers together opinions and ideas shared during the final discussion session at the 1st International Workshop on Multimedia for Cultural Heritage, as a summary of the problems and possible directions to solve to them.

TopHat is a fast splice junction mapper for Next Generation Sequencing analysis, a technology for functional genomic research. Next Generation Sequencing technology allows more accurate analysis increasing data to elaborate, this opens to new challenges in terms of development of tools and computational infrastructures. We present a solution that cover aspects both software and hardware, the first one, after a reverse engineering phase, provides an improvement of algorithm of TopHat making it parallelizable, the second aspect is an implementation of an hybrid infrastructure: grid and virtual grid computing. Moreover the system allows to have a multi sample environment and is able to process automatically totally transparent to user.

This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported. © 2012 Elsevier B.V. All rights reserved.