Publications

Logging information on moving objects is crucial in video surveillance systems. Distributed multi-camera systems can provide the appearance of objects/people from differentviewpoints and at different resolutions, allowing a more complete and precise logging of the information. This is achieved through consistent labeling to correlate collected information of the same person. This paper proposes a novel approach to consistent labeling also capable tofully characterize groups of people and to manage miss segmentations. The ground-plane homography and the epipolar geometry are automatically learned and exploited to warp objects’ principal axes between overlapped cameras. A MAP estimator that exploits two contributions (forward and backward) is used to choose the most probable label con£guration to be assigned at the handoff of a new object. Extensive experiments demonstrate the accuracy of the proposed method in detecting single and simultaneous handoffs, miss segmentations, and groups.

Automatic segmentation of skin lesions in clinical images is a very challenging task; it is necessary for visual analysis of the edges, shape and colors of the lesions to support the melanoma diagnosis, but, at the same time, it is cumbersome since lesions (both naevi and melanomas) do not have regular shape, uniform color, or univocal structure. Most of the approaches adopt unsupervised color clustering. This works compares the most spread color clustering algorithms, namely median cut, k-means, fuzzy-c means and mean shift applied to a method for automatic border extraction, providing an evaluation of the upper bound in accuracy that can be reached with these approaches. Different tests have been performed to examine the influence of the choice of the parameter settings with respect to the performances of the algorithms. Then a new supervised learning phase is proposed to select the best number of clusters and to segment the lesion automatically. Examples have been carried out in a large database of medical images, manually segmented by dermatologists. From these experiments mean shift was resulted the best technique, in term of sensitivity and specificity. Finally, a qualitative evaluation of the goodness of segmentation has been validated by the human experts too, confirming the results of the quantitative comparison.

In this paper, we present a system for the assisted creation of Pictorially Enriched Ontologies, that is ontologies for context-based digital libraries enriched by pictorial concepts for video annotation, summarization and similarity based retrieval. Here we detail the approach for video clips clustering and pictorial concepts extraction together with the approach for storing the ontology within the MPEG-7 framework. The clustering is performed by Complete Link hierarchical clustering on color histograms and motion features. Results on Formula 1 TV material are reported.

In this paper, we approach the problem of detecting editing features suitable for video annotation, by paying attention to artifacts and effects introduced in video editing. In particular, a linear transition detection algorithm is presented, which can characterize the transition center and length with high precision. The technique works with sub-frame granularity and is able to include both abrupt cuts and longer dissolves in a single approach. Theoretical justification for the algorithm is provided with an optimization technique for real cases. We present results obtained exploiting the editing features on a Formula 1 video digital library, detecting replays and providing pre classification hints for automatic shot annotation.

Background/purpose: Since in early melanoma (MM) and especially in in situ MM differential structures, which are diagnostic for MM may be lacking, pigment distribution asymmetry represents an important diagnostic feature. Our aim was to automatically assess pigment distribution in images referring to MMs, atypical nevi (AN) and clearly benign nevi (BN), and to evaluate the diagnostic capability of numerical parameters describing a non homogeneous distribution of pigmentation. Methods: An image analysis program enabling the numerical assessment of pigment distribution in melanocytic lesions (ML), based on evaluation and comparison of red, green, blue (RGB) colour components inside image colour blocks, was employed on 459 videomicroscopic digital images, referring to 95 MMs, 76 AN and 288 BN. Results: Significant differences in pigment distribution parameters (mean RGB distance, variance and maximum distance) between the three ML populations were observed, permitting a good discrimination of MMs. On the test set comprising 230 lesion images, the area under the curve value of the receiver operating characteristic curve was 0.933. For a D score equal to 0, corresponding to the best diagnostic accuracy (86.6%), a sensitivity of 87.5% and a specificity of 85.7% were obtained. Conclusion: This original evaluation method for digital pigment distribution, based on mathematical description and comparison of colours in different image blocks, provides numerical parameters to be implemented in image analysis programs for computer-aided MM diagnosis.

In this paper we describe general algorithms that are devised for MPEG-7 automatic annotation of Formula 1 videos, and in particular for camera-car shots detection. We employed a shot detection algorithm suitable for cuts and linear transitions detection, which is able to precisely detect both the transition's center and length. Statistical features based on MPEG motion compensation vectors arc then employed to provide motion characterization, using a subset of the motion types defined in MPEG-7, and shot type classification. Results on shot detection and classification are provided.

This paper reports the results of an in-depth analysis ofthe degree of usefulness of object shape coding in videocompression. In particular, MPEG-4 is used as referencestandard. The influence of different coding parameters onthe performance is deeply examined and discussions on theresults are provided. Object shape coding is compared withclassical (MPEG-2) frame-based coding both at an objectivelevel (by comparing PSNR/quality and bitrate/filesize)and at a subjective level (asking to a set of users to expresstheir opinion on overall quality, cognitive effectiveness, andwillingness to pay). In conclusion, this paper aims at answering to the question whether it is convenient to use object shape coding instead of frame-based coding or not.

This paper reports advances achieved within a project called LAICA (Laboratorio di Ambient Intelligence per una Città Amica) on Ambient Intelligence in urban environments. The overall LAICA architecture is described and the unified operative centre developed by Regulus SpA (partner of the project) to collect and correlate data from different sensors and prototypes is depicted. Moreover, the paper describes the results obtained in developing a system for video surveillance in public parks, devoted to create a mosaic image of the scene and to extract and track moving people. Moreover, the system takes the privacy issues into account, proposing a method for face detection and tracking able to obscure faces in order to protect people’s identity.