Vitoantonio Bevilacqua

Pseudo 2D Hidden Markov Models for Face Recognition Using Neural Network Coefficients

Face recognition is the preferred mode of identity recognition by humans from an image or video sequence: it is natural, robust and unintrusive. This work presents different pseudo 2D HMM structures for a face recognition showing performances reasonably fast for binary image. The proposed P2-D HMMs are made up of five levels of states, one for each region of interest (Rol) in which the input frontal images are sequenced: forehead, eyes, nose, mouth and chin. Each of P2-D HMMs has been trained by coefficients of an artificial neural network used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. All the P2-D HMMs, applied to the validation set consisting of the Olivetti Research Laboratory (ORL) face database, have achieved good rates of recognition compared to other methods proposed in the literature and, in particular, the structure 3-6-6-6-3 has achieved a rate of recognition equal to 100%.

Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

Abstract Pedestrian detection and tracking have become an important field in the computer vision research area. This growing interest, started in the last decades, might be explained by the multitude of potential applications that could use the results of this research field, e.g. robotics, entertainment, surveillance, care for the elderly and disabled, and content-based indexing. In this survey paper, vision-based pedestrian detection systems are analysed based on their field of application, acquisition technology, computer vision techniques and classification strategies. Three main application fields have been individuated and discussed: video surveillance, human-machine interaction and analysis. Due to the large variety of acquisition technologies, this paper discusses both the differences between 2D and 3D vision systems, and indoor and outdoor systems. The authors reserved a dedicated section for the analysis of the Deep Learning methodologies, including the Convolutional Neural Networks in pedestrian detection and tracking, considering their recent exploding adoption for such a kind systems. Finally, focusing on the classification point of view, different Machine Learning techniques have been analysed, basing the discussion on the classification performances on different benchmark datasets. The reported results highlight the importance of testing pedestrian detection systems on different datasets to evaluate the robustness of the computed groups of features used as input to classifiers.

A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease

The most common methods used by neurologist to evaluate Parkinson’s Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator

A Supervised Breast Lesion Images Classification from Tomosynthesis Technique

In this paper, we propose a deep learning approach for breast lesions classification, by processing breast images obtained using an innovative acquisition system, the Tomosynthesis, a medical instrument able to acquire high-resolution images using a lower radiographic dose than normal Computed Tomography (CT). The acquired images were processed to obtain Regions Of Interest (ROIs) containing lesions of different categories. Subsequently, several pre-trained Convolutional Neural Network (CNN) models were evaluated as feature extractors and coupled with non-neural classifiers for discriminate among the different categories of lesions. Results showed that the use of CNNs as feature extractor and the subsequent classification using a non-neural classifier reaches high values of Accuracy, Sensitivity and Specificity.

A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis

Abstract Patients suffering from Parkinson’s disease are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating Parkinson’s disease patients from healthy subjects by using a handwriting analysis tool based on computer vision and surface ElectroMyoGraphy (sEMG) signal-processing techniques and an Artificial Intelligence-based classifier. Experimental tests have been conducted with both healthy and Parkinson’s Disease patients using the proposed technique to address some specific research scientific questions regarding most representative features, best writing patterns, best AI-based classification approach between ANN optimal topology and SVM approaches in terms of both accuracy and repeatability of the results. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.

Computer Vision and EMG-Based Handwriting Analysis for Classification in Parkinson's Disease.

Handwriting analysis represents an important research area in different fields. From forensic science to graphology, the automatic dynamic and static analyses of handwriting tasks allow researchers to attribute the paternity of a signature to a specific person or to infer medical and psychological patients’ conditions. An emerging research field for exploiting handwriting analysis results is the one related to Neurodegenerative Diseases (NDs). Patients suffering from a ND are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition.

A RGB-D Sensor Based Tool for Assessment and Rating of Movement Disorders

The assessment of tremor features of subjects affected by Parkinson’s disease supports physicians in defining customized rehabilitation treatment which, in turn, can lead to better clinical outcome. In the standard assessment protocol patient performed many exercises that are useful to physicians to rate disease. But the rating is subjective since is based on an observational evaluation. In this paper, we introduce a novel method for achieving objective assessment of movement conditions by directly measuring the magnitude of involuntary tremors with a set of sensors. We focused on one of the standard tasks of the Unified Parkinson’s Disease Rating Scale: finger-to-nose maneuver. During the task, data related to patient finger position are stored and then some tremor’s features are extracted. Finally, we employ a Support Vector Machine to measure the relevance of the extracted features in classify healthy subjects and patients.

A Wearable Device Supporting Multiple Touch- and Gesture-Based Languages for the Deaf-Blind

Over 1.5 million people in the world who are completely deaf-blind use touch-based alphabets to communicate with others and to interact with the world. However, they rely on an assistant who plays the role of an interpreter in translating the world for them. Unfortunately, despite the research work realized in the last decade, on the market there are no assistive devices for providing people who suffer from severe multi-sensory impairments with technology for social inclusion. In this paper, we introduce dbGLOVE, a wearable device for supporting deaf-blind people in being completely independent in communicating with others and in interacting with the world. Our system was co-designed with users to be a natural interface and to accommodate for different already-existing touch- and gesture-based languages, such as Malossi and deaf-blind manual, in order to offer a unique device for connecting different communities with an affordable solution.

Face Recognition, Musical Appraisal, and Emotional Crossmodal Bias

Recent research on the crossmodal integration of visual and auditory perception suggests that evaluations of emotional information in one sensory modality may tend toward the emotional value generated in another sensory modality. This implies that the emotions elicited by musical stimuli can influence the perception of emotional stimuli presented in other sensory modalities, through a top-down process. The aim of this work was to investigate how crossmodal perceptual processing influences emotional face recognition and how potential modulation of this processing induced by music could be influenced by the subjects musical competence. We investigated how emotional face recognition processing could be modulated by listening to music and how this modulation varies according to the subjective emotional salience of the music and the listeners musical competence. The sample consisted of 24 participants: 12 professional musicians and 12 university students (non-musicians). Participants performed an emotional go/no-go task whilst listening to music by Albeniz, Chopin, or Mozart. The target stimuli were emotionally neutral facial expressions. We examined the N170 Event-Related Potential (ERP) and behavioral responses (i.e., motor reaction time to target recognition and musical emotional judgment). A linear mixed-effects model and a decision-tree learning technique were applied to N170 amplitudes and latencies. The main findings of the study were that musicians behavioral responses and N170 is more affected by the emotional value of music administered in the emotional go/no-go task and this bias is also apparent in responses to the non-target emotional face. This suggests that emotional information, coming from multiple sensory channels, activates a crossmodal integration process that depends upon the stimuli emotional salience and the listeners appraisal.

Design of a Projective AR Workbench for Manual Working Stations

We present the design and a prototype of a projective AR workbench for an effective use of the AR in industrial applications, in particular for Manual Working Stations. The proposed solution consists of an aluminum structure that holds a projector and a camera that is intended to be mounted on manual working stations. The camera, using a tracking algorithm, computes in real time the position and orientation of the object while the projector displays the information always in the desired position. We also designed and implemented the data structure of a database for the managing of AR instructions, and we were able to access this information interactively from our application.