Publications

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.

The purpose of this paper is to describe the work in progress related to the design, the implementation and the evaluation of an innovative e-learning platform for ICT individual training in the framework of an EC funded project named Diogene. The present e-learning solution includes several state-of-the-art technologies and methodologies such as: metadata and ontologies for knowledge manipulation, fuzzy learner modelling, intelligent course tailoring, co-operative and online training support. The proposed solution is based on the distribution of working tasks among content provider services, content discovery services, content brokering services, training services, curriculum vitae searching services and collaboration services.

In this paper we address the problem of human posture classification, in particular focusing to an indoor surveillance application. The approach was initially inspired to a previous works of Haritaoglou et al. [6] that uses histogram projections to classify people’s posture. Projection histograms are here exploited as the main feature for the posture classification, but, differently from [6], we propose a supervised statistical learning phase to create probability maps adopted as posture templates. Moreover, camera calibration and homography is included to resolve prospective problems and improve the precision of classification. Furthermore, we make use of a finite state machineto detect dangerous situations as falls and to activate a suitable alarm generator. The system works on line on standard workstation with network cameras.

Typical processes of image representation comprehend initial region segmentation followed by a description of single regions’ feature and their relationships. Then a graph model can be exploited in order to integrate the knowledge of the specific regions (that are the attributed relational graph’s (ARG) nodes) and the regions’ relations (that are the ARG’s edges). In this work we use color features to guide region segmentation, geometric features to characterize regions one by one and topological features (and in particular inclusion) to describe regions’ relationships. Guided by the inclusion property we define the Topological Tree (TT) as an image representation model that exploiting the transitive property of inclusion, uses the adjacency and inclusion topological features. We propose an approach based on a recursive version of fuzzy c-means to construct the topological tree directly from the initial image, performing both segmentation and TT construction. The TT can be exploited in many applications of image analysis and image retrieval by similarity in those contexts where inclusion is a key feature: we propose an applicative case of analysis of dermatological images to support the melanoma diagnosis.In this paper describe details of the TT algorithm, including the management of not ideality and an approximate measure of tree similarity in order to retrieve skin lesion with a similar TT-based description.

In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy [6], that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithmwith a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.

In this paper we propose a vision-based system that lets the robot recognize an environment observed through the construction of a perspective structure which characterizes it. The individualization of the most significant characteristics of the perspective structure is performed by a geometric method that, using the information given by the image, represents the scene through elementary geometrical forms (such as straight lines) and, on the basis of this geometrical representation, it detects the perspective structure elements (e.g. the vanishing point). The method returns results that can help the robot to establish if he is really inside a corridor or in another place.

In this work we present a framework for on-the-fly video transcoding that exploits computer vision-based techniques to adapt the Web access to the user requirements. Theproposed transcoding approach aims at coping with both user bandwidth and resources capabilities, and with user interests in the video's content. We propose an object-basedsemantic transcoding that, according to the user-dened classes of relevance, applies different transcoding techniques to the objects segmented in a scene. Object extraction is provided by on-the-fly video processing, without manual annotation. Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error (and corresponding PSNR), that takes into account the perceptual user requirements by means of classes of relevance, is dened. Results are analyzed by varying transcoding techniques, bandwidth requirements and video types (with indoor and outdoor scenes), showing that the use of semantics can dramatically improve the bandwidth to distortion ratio.

In this work we present a transcoding framework and an object-based technique to adapt live and stored videos to the user bandwidth and resources capabilities.Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error that allows different classes of relevance is presented.We present results for different transcoding policies and for different bandwidth requirements, showing that the use of semantic can improve the bandwidth to distortion ratio.