Search results for " informazioni"

Approximated overlap error for the evaluation of feature descriptors on 3D scenes

2013

This paper presents a new framework to evaluate feature descriptors on 3D datasets. The proposed method employs the approximated overlap error in order to conform with the reference planar evaluation case of the Oxford dataset based on the overlap error. The method takes into account not only the keypoint centre but also the feature shape and it does not require complex data setups, depth maps or an accurate camera calibration. Only a ground-truth fundamental matrix should be computed, so that the dataset can be freely extended by adding further images. The proposed approach is robust to false positives occurring in the evaluation process, which do not introduce any relevant changes in the …

Software Architectures for Human-Machine Interaction Using Natural Language

Il linguaggio naturale rappresenta un sistema di comunicazione a carattere inferenziale in opposizione ai sistemi di comunicazione a codice che non prevedono una forma di ragionamento intelligente da parte del ricevente, ma si basano sul riconoscimento di patterns dell'informazione. In un sistema di comunicazione di tipo inferenziale, infatti, si parte dal presupposto che il ricevente abbia una certa "intelligenza" e sia, quindi, capace di comprendere, elaborare ed inferire il contenuto informativo di una comunicazione attraverso ragionamenti su un background di conoscenze (come modelli di mondo e di linguaggio) condivisi sia dalla sorgente che dal destinatario. L'attività di ricerca, svolt…

Creation and cognition for humanoid live dancing

2016

Abstract Computational creativity in dancing is a recent and challenging research field in Artificial Intelligence and Robotics. We present a cognitive architecture embodied in a humanoid robot capable to create and perform dances driven by the perception of music. The humanoid robot is able to suitably move, to react to human mate dancers and to generate novel and appropriate sequences of movements. The approach is based on a cognitive architecture that integrates Hidden Markov Models and Genetic Algorithms. The system has been implemented on a NAO robot and tested in public setting-up live performances, obtaining positive feedbacks from the audience.

An automatic system for humanoid dance creation

2016

Abstract The paper describes a novel approach to allow a robot to dance following musical rhythm. The proposed system generates a dance for a humanoid robot through the combination of basic movements synchronized with the music. The system made up of three parts: the extraction of features from audio file, estimation of movements through the Hidden Markov Models and, finally, the generation of dance. Starting from a set of given movements, the robot choices sequence of movements a suitable Hidden Markov Model, and synchronize them processing musical input. The proposed approach has the advantage that movement execution probabilities could be changed according evaluation of the dance executi…

An efficient distributed algorithm for generating and updating multicast trees

2006

As group applications are becoming widespread, efficient network utilization becomes a growing concern. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in networks) is prohibitively expensive. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem, allowing the construction of effective distribution trees using a coordination protocol among the network nodes. Furthermore, we propose a novel distribut…

Information Assurance and Advanced Human-Computer Interfaces

2008

Fuzzy Clustering of Histopathological Images Using Deep Learning Embeddings

2021

Metric learning is a machine learning approach that aims to learn a new distance metric by increas- ing (reducing) the similarity of examples belonging to the same (different) classes. The output of these approaches are embeddings, where the input data are mapped to improve a crisp or fuzzy classifica- tion process. The deep metric learning approaches regard metric learning, implemented by using deep neural networks. Such models have the advantage to discover very representative nonlinear embed- dings. In this work, we propose a triplet network deep metric learning approach, based on ResNet50, to find a representative embedding for the unsupervised fuzzy classification of benign and maligna…

UnipaBCI a novel general software framework for brain computer interface

2017

The increasing interest in Brain Computer Interface (BCI) requires new fast, reliable and scalable frameworks that can be used by researchers to develop BCI based high performance applications in efficient and fast ways. In this paper is presented "UnipaBCI", a general software framework for BCI applications based on electroencephalogra-phy (EEG) that can fulfill these new needs. A visual evoked potentials (VEP) application has also been developed using the proposed framework in order to test the modular architecture and the overall performance. Different types of users (beginners and experts in BCI) have been involved during the "UnipaBCI" experimental test and they have exhibited good and…

Touch or touchless?:Evaluating usability of interactive displays for persons with autistic spectrum disorders

2019

Interactive public displays have been exploited and studied for engaging interaction in several previous studies. In this context, applications have been focused on supporting learning or entertainment activities, specifically designed for people with special needs. This includes, for example, those with Autism Spectrum Disorders (ASD). In this paper, we present a comparison study aimed at understanding the difference in terms of usability, effectiveness, and enjoyment perceived by users with ASD between two interaction modalities usually supported by interactive displays: touch-based and touchless gestural interaction. We present the outcomes of a within-subject setup involving 8 ASD users…

Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging

2017

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T…