Publications

The aim of this study was to provide mathematical descriptors for the border of pigmented skin lesion images and to assess their efficacy for distinction among different lesion groups. New descriptors such as lesion slope and lesion slope regularity are introduced and mathematically defined. A new algorithm based on the Catmull-Rom spline method and the computation of the gray-level gradient of points extracted by interpolation of normal direction on spline points was employed. The efficacy of these new descriptors was tested on a data set of 510 pigmented skin lesions, composed by 85 melanomas and 425 nevi, by employing statistical methods for discrimination between the two populations.

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.

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.

We propose a Web tutoring system in which Artificial Intelligence techniques and Semantic Web approaches are integrated in order to provide an automatic tool able both to completely customize learning on the student’s needs and to exchange learning material with other Web systems. IWT (Intelligent Web Teacher) is based on an ad hoc knowledge representation which describes the didactic domain by means of an Ontology. The student can select the concepts belonging to the Ontology she/he is interested in which. The system planning mechanism builds the most suitable Learning Path for that student.

Background The assessment of colours is essential for the diagnosis of malignant melanoma ( MM), both for pattern analysis on dermoscopic images, and when employing semiquantitative methods. Objectives To develop a computer program for colour assessment in MM images mimicking the human perception of lesion colours, and to compare the automatic colour evaluation with one performed by human observers. Methods A colour palette comprising six colour groups ( black, dark brown, light brown, blue grey, red and white) was created by selecting single colour components inside melanocytic lesion images acquired by means of a digital videomicroscope, and was implemented in the image analysis program. Subsequently, colours were assessed by the computer program on 331 melanocytic lesion images composing our image database, and the results were compared with the evaluation of lesion colours performed by the clinician. Results The black, white and blue - grey colours were more frequently found in MMs than in naevi, both by the clinicians and by the computer. In MM images we observed 4.27 +/- 1.14 colours (mean +/- SD) per lesion, as opposed to 3.22 +/- 0.68 in naevi. The correlation between clinical and computer evaluation of the colours was very good, with a value of 0.781 for overall assessment. Conclusions This innovative method for automatic colour evaluation, reproducing clinical assessment of melanocytic lesion colours, may provide numerical parameters to be employed for computer-aided diagnosis of MM.

The description of the border appears to be an important feature for clinical judgement in dermatoscopy, but it is subjective and can lead to different results depending on the examiners’ experience. In order to provide mathematical descriptors for border regularity and to increase the reproducibility of clinical judgement, a method to quantify border characteristics and to automatically reproduce the B (Border) parameters of the ABCD rule was developed. 331 images of pigmented skin lesions, 113 referring to melanomas and 218 to melanocytic naevi, acquired by a digital videomicroscope with a 20× magnification were studied. Clinical evaluation: for the evaluation of border cut-off, a score ranging to 0 from 8 was attributed to each lesion on the basis of the number of segments with an abrupt edge interruption of the pigmentation. Computer elaboration: after automatic border detection, the skin lesion gradient, defined as the change in lightness values along a 30 pixel long segment centred on the lesion border, expressed as the slope of the curve, was calculated along a 30 pixel segment. Minimum and maximum values and the standard deviation were calculated for the description of border regularity. In order to compare clinical and computer evaluation, the lesion border was divided into 8 segments and a threshold for abrupt border cut-off was set on a visual basis. Melanomas presented more abrupt and inhomogeneous margins in respect of melanocytic naevi. A good correlation between clinical evaluation and computer elaboration was found for the number of borders with an abrupt cut-off (rho = 0.834; P < 0.001). Computerized image analysis appears to be able to numerically describe pigmented skin lesions and to reproduce some aspects of the clinical evaluation. Enabling an objective and reproducible description, it could represent a useful support to clinical diagnosis.

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 propose an approach to indoor environment surveillance and, in particular, to people behaviour control in home automation context. The reference application is a silent and automatic control of the behaviour of people living alone in the house and specially conceived for people with limited autonomy (e.g., elders or disabled people). The aim is to detect dangerous events (such as a person falling down) and to react to these events by establishing a remote connection with low-performance clients, such as PDA (Personal Digital Assistant). To this aim, we propose an integrated server architecture, typically connected in intranet with network cameras, able to segment and track objects of interest; in the case of objects classified as people, the system must also evaluate the people posture and infer possible dangerous situations. Finally, the system is equipped with a specifically designed transcoding server to adapt the video content to PDA requirements (display area and bandwidth) and to the user's requests. The main issues of the proposal are a reliable real-time object detector and tracking module, a simple but effective posture classifier improved by a supervised learning phase, and an high performance transcoding inspired on MPEG-4 object-level standard, tailored to PDA. Results on different video sequences and performance analysis are discussed.