Publications

Pixel classifiers are often adopted in pattern recognition as a suitable method for image segmentation. A common approach to the performance evaluation of classifier systems is based on the measurement of the classification errors and, at the same time, on the computational time. In general, multiclassifiers have proven to be more precise in the classification in many applications, but at the cost of a higher computational load. This paper analyzes different classifiers and proposes an evaluation of the classifiers in the case of semi-automatic processes with human interaction. Medical imaging is a typical application, where automatic or semi-automatic segmentation can be a valuable support to the diagnosis. The paper focuses on the segmentation of cardiac images of fruit flies (genetic model for analyzing human heart's diseases). Analysis is based on M-modes, that are gray-level images derived from mono-dimensional projections of the video frames on a line. Segmentation of the M-mode images is provided by classifiers and integrated in a multiclassifier. A neural network classifier, a Bayesian classifier, and a classifier based on hidden Markov chains are joined by means of a Behavior Knowledge Space fusion rule. The comparative evaluation is discussed in terms of both accuracy and required time, in which the time to correct the classifier errors by means of human intervention is also taken into account.

In this work, we present a general purpose system for hierarchical structural segmentation and automatic annotation of video clips, by means of standardized low level features. We propose to automatically extract some prototypes for each class with a context based intra-class clustering. Clips are annotated following the MPEG-7 standard directives to provide easier portability. Results of automatic annotation and semiautomatic metadata creation are provided

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 an advanced video surveillance system is proposed.Our goal is the detection of the people’s heads toallow their obscuration for privacy issues or to performrecognition tasks. We propose a system based on active PTZ(Pan-Tilt-Zoom) cameras that produce head images havinga large enough size, and can cover an area larger than stillcameras. Since conventional approaches are not suitable toPTZ cameras, the proposed approach is based on the socalleddirection histograms to compute the ego-motion andon frame differencing for detecting moving objects. It exploitspost-processing and active contours to extract preciseshape of moving objects to be fed to a probabilistic algorithmto track moving people in the scene. Person following,instead, is based on simple heuristic rules that movethe camera as soon as the selected person is close to theborder of the field of view. Finally, a color and shape basedhead detection that takes advantage of the people trackingis presented. Experimental results on a live active camerademonstrate the feasibility of real-time person followingand of the consecutive head detection phase.

Background Semiquantitative algorithms were applied to dermoscopic images to improve the clinical diagnosis for melanoma. Objective The aim of the study was to develop a computerized method for automated quantification of the 'A' (asymmetry) and 'B' (border cut-off) parameters, according to the ABCD rule for dermoscopy, thus reproducing human evaluation. Methods Three hundred and thirty-one melanocytic lesion images, referring to 113 melanomas and 218 melanocytic nevi, acquired by means of a digital videodermatoscope, were considered. Images were evaluated by two experienced observers and by using computer algorithms developed by us. Clinical evaluation of asymmetry was performed by attributing scores to shape asymmetry and asymmetry of pigment distribution and structures, whereas computer evaluation of shape and pigment distribution asymmetries were based on the assessment of differences in area and lightness in the two halves of the image, respectively. Borders were evaluated both by clinicians and by the computer, by attributing a score to each border segment ending abruptly. Differences between nevus and melanoma values were evaluated using the chi-square test, while Cohen's Kappa index for agreement was employed for the evaluation of the concordance between human and computer. Results Pigment distribution asymmetry appears the most striking parameter for melanoma diagnosis both for human and for automated diagnosis. A good concordance between clinicians and computer evaluation was achieved for all asymmetry parameters, and was excellent for border cut-off evaluation. Conclusions These algorithms enable a good reproduction of the 'A' and 'B' parameters of the ABCD rule for dermoscopy, and appear useful for diagnostic and learning purposes.

Digital dermoscopy improves the accuracy of melanoma diagnosis. The aim of this study was to develop and validate software for assessment of asymmetry in melanocytic lesion images, based on evaluation of colour symmetry, and to compare it with assessment by human observers. An image analysis program enabling numerical assessment of asymmetry in melanocytic lesions, based on the evaluation and comparison of CIE L*a*b* colour components (CIE L*a*b* is the name of a colour space defined by the Commission Internationale de l'Eclairage) inside image colour blocks, was employed on the recorded lesion images. Clinical evaluation of asymmetry in dermoscopic images was performed on the same image set employing a 0-1 scoring system. Asymmetry judgement was expressed by the clinicians for 12.8% of benign naevi, 44.7% of atypical naevi and 64.2% of malignant melanomas, whereas the computer identified as asymmetric 6.3%, 33.3% and 82.2%, respectively. Numerical parameters referring to malignant melanomas were significantly higher, both with respect to benign naevi and atypical naevi. The numerical parameters produced could be effectively employed for computer-aided melanoma diagnosis.

In this paper an automated assessment of pigment distribution and color areas for melanoma diagnosis is described.