Publications

In this paper we present a distributed videosurveillanceframework. Our end is the remote monitoringof the behavior of people moving in a scene exploitinga virtual reconstruction on low capabilitiesdevices, like PDAs and cell phones. The main noveltyof this system is the effective integration of the computervision and computer graphics modules. The first,using a probabilistic frameworks, can detect the position,the trajectory and the posture of peoples movingin the scene. The second exploits the new possibility ofboth standard 3D graphics libraries on mobile (namelyJSR184 and M3G graphic format) and new PDAsprocessing capability in order to reconstruct the remotesurveillance data in real-time.

Effective usage of multimedia digital libraries has to deal with the problem of building efficient content annotation and retrieval tools. MOM (Multimedia Ontology Manager) is a complete system that allows the creation of multimedia ontologies, supports automatic annotation and creation of extended text (and audio) commentaries of video sequences, and permits complex queries by reasoning on the ontology.

This paper proposes a new human-machine interface particularly conceived for people with severe disabilities (specifically tetraplegic people), that allows them to interact with the computer for their everyday life by means of mouse pointer. In this system, called FaceMouse, instead of classical pointer paradigm that requires the user to look at the point where to move, we propose to use a paradigm called derivative paradigm, where the user does not indicate the precise position, but the direction along which the mouse pointer must be moved. The proposed system is composed of a common, lowcost webcam, and by a set of computer vision techniques developed to identify the parts of the user's face (the only body part that a tetraplegic person can move) and exploit them for moving the pointer. Specifically, the implemented algorithm is based on template matching to track the nose of the user and on cross-correlation to calculate the best match. Finally, several real applications of the system are described and experimental results carried out by disabled people are reported.

This paper describes a complete approach to detect, localize and describe network patterns. Such texture is automatically detected with Gaussian derivative kernels and Fisher linear discriminant analysis; line closure and thinning is provided by morphological masking and line luminance profile fitting provides width estimation. Detection results on dermatological images are reported and discussed.

In this paper a practical color calibration procedure for dermatoscopic image acquisition is illustrated, with details on the algorithms employed and results on real data.

This paper proposes a novel method for estimating thegeospatial trajectory of a moving camera. The proposedmethod uses a set of reference images with known GPS(global positioning system) locations to recover the trajectoryof a moving camera using geometric constraints. Theproposed method has three main steps. First, scale invariantfeatures transform (SIFT) are detected and matched betweenthe reference images and the video frames to calculatea weighted adjacency matrix (WAM) based on the numberof SIFT matches. Second, using the estimated WAM, themaximum matching reference image is selected for the currentvideo frame, which is then used to estimate the relativeposition (rotation and translation) of the video frame usingthe fundamental matrix constraint. The relative position isrecovered upto a scale factor and a triangulation amongthe video frame and two reference images is performed toresolve the scale ambiguity. Third, an outlier rejection andtrajectory smoothing (using b-spline) post processing stepis employed. This is because the estimated camera locationsmay be noisy due to bad point correspondence or degenerateestimates of fundamental matrices. Results of recoveringcamera trajectory are reported for real sequences.

This work proposes a method for automatic face obscuration capable of protecting people's identity. Since face detection heavily benefits from the possibility to exploit tracking, multi-camera people tracking has been integrated with a face detector based on colour clustering and Hough transform. Moreover, the multiple viewpoints provided by multiple cameras are exploited in order to always obtain a good-quality image of the face. The identity of people in different views is kept consistent by means of a geometrical, uncalibrated approach based on homographies. Experimental results show the accuracy of the proposed approach. (c) 2006 Elsevier B.V. All rights reserved.

In this paper we focus on the problem of automatically registering dermatological images, because even if different products are available, most of them share the problem of a limited field of view on the skin. A possible solution is then the composition of multiple takes of the same lesion with digital software, such as that for panorama images creation.In this work, to perform an automatic selection of matching points the Harris Corner Detector is used, and to cope with outlier couples we employed the RANSAC method. Projective mapping is then used to match the two images. Given a set of correspondence points, Singular Value Decomposition was used to compute the transform parameters.At this point the two images need to be blended together. One initial assumption is often implicitly made: the aim is to merge two rectangular images. But when merging occurs between more than two images iteratively, this assumption will fail. To cope with differently shaped images, we employed the Distance Transform and provided a weighted merging of images. Different tests were conducted with dermatological images, both with standard rectangular frame and with not typical shapes, as for example a ring due to the objective and lens selection. The successive composition of different circular images with other blending functions, such as the Hat function, doesn’t correctly get rid of the border and residuals of the circular mask are still visible. By applying Distance Transform blending, the result produced is insensitive of the outer shape of the image.

A system for video surveillance purposes in wide areas based on active cameras, also capable to follow a person in the scene by keeping him framed, is presented. The proposed approach is based on the so-called direction histograms to compute the ego-motion and on frame differencing for detecting moving objects. It exploits post-processing and active contours to extract precise shape of moving objects to be fed to a probabilistic algorithm to track moving people in the scene. Person following, instead, is based on simple heuristic rules that move the camera as soon as the selected person is close to the border of the field of view. Experimental results on a live active camera demonstrate the feasibility of real-time person following.