Interfacce uomo-macchina nella realtà virtuale

Questo capitolo fornisce una descrizione dei principali elementi che influenzano l'interazione uomo-macchina in riferimento alla realtà virtuale, per come si configurano attualmente, e per come si prevede si svilupperanno in un prossimo futuro. Il capitolo è organizzato nel modo seguente: la sezione 1.1 presenta il concetto di realtà virtuale soprattutto in relazione alle possibilità offerte per quanto riguarda l’interazione tra uomo e macchina, ed alle applicazioni di nuova generazione. La sezione successiva descrive i principali requisiti ed i vincoli che un sistema di realtà virtuale deve soddisfare per riuscire a fornire all’utente un’impressione convincente e delle esperienze realmente immersive. La sezione 1.3 si concentra sul feedback sensoriale principale, descrivendo le principali tecnologie di nuova generazione per la realizzazione di dispositivi in grado di fornire delle sensazioni visive e tattili estremamente realistiche. Infine la sezione 1.4 descrive brevemente alcuni esempi di applicazioni di realtà virtuale realizzate dagli autori, nel campo della simulazione chirurgica, dei musei virtuali e dei sistemi di visualizzazione autostereoscopici multiutente, e la sezione 1.5 discute brevemente la situazione attuale ed il potenziale futuro della disciplina.289-33

View-dependent Exploration of Massive Volumetric Models on Large Scale Light Field Displays

We report on a light-field display based virtual environment enabling multiple naked-eye users to perceive detailed multi-gigavoxel volumetric models as floating in space, responsive to their actions, and delivering different information in different areas of the workspace. Our contributions include a set of specialized interactive illustrative techniques able to provide different contextual information in different areas of the display, as well as an out-of-core CUDA based raycasting engine with a number of improvements over current GPU volume raycasters. The possibilities of the system are demonstrated by the multi-user interactive exploration of 64GVoxels datasets on a 35MPixel light field display driven by a cluster of PCs.1037-1047Pubblicat

An energy preserving upscaling technique for enhanced volume rendering of medical data

Proc. 3D Anatomical Human Summer School 2010, 23-24 May, Chania, Greece: EU Marie Curie Research Training Network. 2010.In this paper we describe an edge-directed optimization-based method for volumetric data supersampling. Our method faces the problem of partial volume effect by upscaling the volumetric data, subdividing voxels in smaller parts and performing an optimization step keeping constant the energy of each original subdivided voxel while enhancing edge continuity. Experimental tests show the good quality of the results obtained with our approach. Furthermore, we show how offline 3D upscaling of volumes can be coupled with recent techniques to perform high quality volume rendering of large datsets, obtaining a better inspection of medical volumetric data.In corso di stamp

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.85-95Pubblicat

Highway Remodeling: Harnessing Georeferenced Data for Procedural Modeling

Cursos e Congresos, C-155[Abstract] : This paper introduces a novel procedural modeling system for generating 3D highway models, leveraging real-world data inputs such as highway layouts and manual annotations of essential elements. Our highly parameterized system facilitates easy customization, including modifications to the number of lanes and other key features, broadening its applicability across diverse domains, from urban planning and transportation engineering to virtual simulationsGeneralitat Valenciana; CIBEST/2022/139Xunta de Galicia; ED431F 2021/11This work has been supported by the Spanish Ministry of Science and Innovation (AEI/PID2020-115734RB-C22). The whole team also wants to acknowledge the support provided by Side Effects Software Inc. in developing our work. Jose Ribelles was supported by Generalitat Valenciana (CIBEST/2022/139). Javier Taibo and J.A. Iglesias-Guitian were supported by Xunta de Galicia (ED431F 2021/11). Additionally, J.A. Iglesias-Guitian also acknowledges the UDC-Inditex InTalent programme and the Ministry of Science and Innovation (AEI/RYC2018-025385-I

Collaborative Semantic Content Management: an Ongoing Case Study for Imaging Applications

This paper presents a collaborative solution for knowledge management, implemented as a semantic content management system (CMS) with the purpose of knowledge sharing between users with different backgrounds. The CMS is enriched with semantic annotations, enabling content to be categorized, retrieved and published on the Web thanks to the Linked Open Data (LOD) principle which enables the linking of data inside existing resources using a standardized URI mechanism. Annotations are done collaboratively as a social process. Users with different backgrounds express their knowledge using structured natural language. The user knowledge is captured thanks to an ontologic approach and it can be further transformed into RDF(S) classes and properties. Ontologies are at the heart of our CMS and they naturally co-evolve with their communities of use to provide a new way of knowledge sharing inside the network. The ontology is modeled following the so-called DOGMA (Developing Ontology-Grounded Methods and Applications) paradigm, grounded in natural language. The approach will be demonstrated on a use case concerning the semantic annotation of anatomical data (e.g. medical images).257-26

Variable Rate Deep Image Compression with Modulated Autoencoder

Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods are optimized for a single fixed rate-distortion tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bit rates. Addressing these limitations, we formulate the problem of variable rate-distortion optimization for deep image compression, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific rate-distortion tradeoff via a modulation network. Jointly training this modulated autoencoder and modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.Comment: Published as a journal paper in IEEE Signal Processing Letter

An interactive 3D medical visualization system based on a light field display

This paper presents a prototype medical data visualization system exploiting a light field display and custom direct volume rendering techniques to enhance understanding of massive volumetric data, such as CT, MRI, and PET scans. The system can be integrated with standard medical image archives and extends the capabilities of current radiology workstations by supporting real-time rendering of volumes of potentially unlimited size on light field displays generating dynamic observer-independent light fields. The system allows multiple untracked naked-eye users in a sufficiently large interaction area to coherently perceive rendered volumes as real objects, with stereo and motion parallax cues. In this way, an effective collaborative analysis of volumetric data can be achieved. Evaluation tests demonstrate the usefulness of the generated depth cues and the improved performance in understanding complex spatial structures with respect to standard techniques.883-893Pubblicat