Francesco La Rosa

Quantitative Analysis of Pursuit Ocular Movements in Parkinson’s Disease by Using a Video-Based Eye Tracking System

The purpose of this study is to assess the efficacy and the tolerability of a new vision-based non-intrusive eye tracker in a population composed of normal controls and in patients affected by nonadvanced Parkinson’s disease (PD). PD patients characteristically have difficulty in sustaining repetitive motor actions. Previous studies showed a progressive bradykinesia and hypokinesia of pursuit ocular movements (POM) in advanced PD. We found that the values of POM were lower in PD patients than in normal controls (p < 0.001). In PD patients, the values correlated closely with Hoehn and Yahr stage and Unified Parkinson Disease Rating Scale motor subscore (p < 0.001, for both). Our data suggest that deficit in POM occurs also in nonadvanced PD patients and it is closely correlated with clinical scores. Thus, this vision-based system can be considered a new method to provide, noninvasively, measures of POM dysfunctions and can be used as reliable indices of disease severity in PD patients.

A vision-based system for elderly patients monitoring

Remote patient monitoring can improve the quality of life of elderly and impaired people, while reducing the costs. Among the most interesting technologies being investigated, computer vision has proved to be very effective in several important scenarios in which conventional sensors fail or are impractical. We propose a computer vision-based wireless sensor system for people remote tracking and monitoring based on low-cost embedded systems able to visually track the patient and detect critical motion and posture patterns, associated with dangerous situations. Motivation for the work and experimental results are provided, showing the effectiveness and the validity of the presented approach.

A multimodal perceptual user interface for video-surveillance environments

In this paper a perceptual user interface (PUI) for video-surveillance environments is introduced. This system provides a tool for a video-surveillance control-room, and exploits a novel multimodal user interaction paradigm based on hand gesture and perceptual user interfaces. The proposed system, being simple and intuitive, is expected to be useful in the control of large and dynamic environments. To illustrate our work, we introduce a proof-of concept multimodal, bare-hand gesture-based application and discuss its implementation and the obtained experimental results.

A SIFT-Based Fingerprint Verification System Using Cellular Neural Networks

Recently, with the increasing demand of high security, person identification has become more and more important in our everyday life. The purpose of establishing the identity is to ensure that only a legitimate user, and not anyone else, accesses the rendered services. The traditional identification methods are based on “something that you possess” and “something that you know” such as key, user-ID, password, PIN, etc. Examples of such applications include secure access to buildings, airports, computer systems, cellular phones and ATM machines. Another family of identification methods uses biometric characteristics. Biometric recognition, or simply biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. Biometrics allows us to confirm or establish an individual’s identity based on who she is, rather than by what she possesses (e.g., an ID card) or what she knows (e.g., a password). Current biometric systems make use of identifiers such as fingerprints, hand geometry, iris, face and voice to establish an identity. Biometric systems also introduce an aspect of user convenience. For example, they alleviate the need for a user to remember multiple passwords associated with different applications. Fingerprint characterization is the oldest and the prevalent member of the biometric family and has been extensively used for person identification in a number of commercial, civil and forensic applications. The question that is being asked about biometric technologies in general and about fingerprints in particular is that whether these technologies can work all the time, everywhere, and in all contexts for reliable person identification and authentication. One of the design criteria for building such completely automatic and reliable fingerprint identification (and verification) systems is that the underlying sensing, representation, and matching technologies must also be very robust. In practice, due to variations in impression conditions, ridge configuration, skin conditions (aberrant formations of epidermal ridges of fingerprints, postnatal marks, occupational marks), acquisition devices and non-cooperative attitude of subjects a significant percentage of acquired fingerprint images is of poor quality. In order to ensure that the performance of a feature extraction algorithm will be robust with respect to the quality of input fingerprint images, an enhancement algorithm which can improve the clarity of the ridge structures is useful. Most of the fingerprint image enhancement methods (Gabor, directional or anisotropic filter based) use convolution to obtain the results. Another way to address these O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

A multimodal perceptual user interface for collaborative environments

In this paper a 3D graphics-based remote collaborative environment is introduced. This system is able to provide multiclient and multimedia communication, and exploits a novel multimodal user interaction paradigm based on hand gesture and perceptual user interfaces. The use of machine vision technologies and a user-centered approach produce a highly usable and natural human-computer interface, allowing even untrained users a realistic and relaxing experience for long and demanding tasks. We then note and motivate that such an application can be considered as an Augmented Reality application; according to this view, we describe our platform in terms of long-term usability and comfort of use. The proposed system is expected to be useful in remote interaction with dynamic environments. To illustrate our work, we introduce a proof-of-concept multimodal, bare-hand application and discuss its implementation and the obtained experimental results.

A wireless sensor network for distributed autonomous traffc monitoring

Automatic traffic monitoring and surveillance have become essential for effective road usage and management. Various sensors have been used to estimate traffic parameters, but their installation and maintenance is often difficult and costly. Among the technologies being investigated, computer vision promises the most flexible and reliable solutions to estimate traffic parameters. This paper proposes a wireless sensor network (WSN) architecture for autonomous traffic monitoring, based on computer vision techniques for automatic scene analysis and interpretation. The paper first discusses the motivation for the work and the relevant design issues. Then, the proposed architecture and the relevant modules are described in detail. Finally, experimental results are shown, which prove the accuracy of the proposed approach.

A Real-Time Solution to the Image Segmentation Problem: CNN-Movels

2D Image segmentation has been a main issue in image analysis since the very early years. Traditional literature usually classifies segmentation approaches as area-based or contourbased. In the second class, among dozens of different approaches, Active Contours have recently gained more and more interest. Active contours (also known as deformable models) are open or closed curves that can accurately fit to the contours of objects featuring almost any kind of shape. These models are called active because they automatically respond to specific characteristics of the points of the image, by changing their shape consequently. For example, an active contour can respond to the edgeness values of the image points. A particular type of active contour is the snake: it responds both to the characteristics of the points of the image (through the minimization of a quantity called external energy), and to specific internal laws ruling its shape and way of deformation, tending to minimize a quantity called internal energy (Kass et al., 1988; Lai & Chin, 1995). It usually consist of elastic curves that, located over an image, evolve from their initial shapes and positions in order to adapt themselves to the notable characteristics of the scene. This evolution comes as a result of the combined action of external and internal forces. The external forces lead the snakes towards features of the image, whereas internal forces model the elasticity of the curves. In a parametric representation, a snake appears as a curve u(s)=(x(s),y(s)), s [0,1], with u(0)=u(1). Its internal energy is often defined as