1,721,114 research outputs found
Encoding immersive sessions for online, interactive VR analytics
No full textCapturing and recording immersive VR sessions performed through HMDs in explorative virtual environments may offer valuable insights on users’ behavior, scene saliency and spatial affordances. Collected data can support effort prioritization in 3D modeling workflow or allow fine-tuning of locomotion models for time-constrained experiences. The web with its recent specifications (WebVR/WebXR) represents a valid solution to enable accessible, interactive and usable tools for remote VR analysis of recorded sessions. Performing immersive analytics through common browsers however presents different challenges, including limited rendering capabilities. Furthermore, interactive inspection of large session records is often problematic due to network bandwidth or may involve computationally intensive encoding/decoding routines. This work proposes, formalizes and investigates flexible dynamic models to volumetrically capture user states and scene saliency during running VR sessions using compact approaches. We investigate image-based encoding techniques and layouts targeting interactive and immersive WebVR remote inspection. We performed several experiments to validate and assess proposed encoding models applied to existing records and within networked scenarios through direct server-side encoding, using limited storage and computational resources
Shape description and recognition by a multiresolution approach
No full textThis paper presents a multiresolution approach that uses a diffusion process to describe the shape of a 2D object. As a result, shape recognition can be achieved: shape contours may be recognized independently from orientation or size. The method proposed relies on the concept of a structural coding of an object at varying levels of resolution. A tree structure represents the evolution of the contour at increasing levels of detail, where each tree node represents a contour segment via a set of attributes to provide a richer description of the image shape. The shape recognition is based on a matching of the attributed tree representation of the candidates with that of the model
A multiresolution approach for image coding and transmission for traffic scene monitoring via web
No full textIntelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems
No full text"In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environ- ments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordination and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.
Evaluating digital angles by a parallel diffusion process
No full textCorners provide rich information for examining frame-to-frame displacement characteristics of images, and are very useful features for such purposes as image matching or shape analysis. This paper describes a new method that uses a diffusion-like process to classify angles in an image. The proposed method exploits the curvature value of a contour to characterize the corresponding angle. The effectiveness of the new method is illustrated with the results of experiments for a number of different angle types
eXEDRA: A complete open source architecture for paper document recognition
No full textThe Automatic Document recognition is fundamental for office automation becoming every day a more powerful tool in those fields where information is still on paper. Document recognition follows from data acquisition, from both journals, and entire books in order to transform them in digital objects. We present a new architecture for Document recognition that follows the Open Source methodologies for documents segmentation and classification, which turns to be beneficial in terms of computation efficiency, general-purpose availability and cost. ©2003 IEEE
Guidelines for effective automatic multiple sclerosis lesion segmentation by magnetic resonance imaging
No full textGeneral constraints for automatic identification/segmentation of multiple sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) are discussed and guidelines for effective training of a supervised technique are presented. In particular, system generalizability to different imaging sequences and scanners from different manufacturers, misalignment between images from different modalities and subjectivity in generating labelled images, are indicated as the main limitations to high accuracy automatic MS lesions identification/segmentation. A convolutional neural network (CNN) based method is used by applying the suggested guidelines and preliminary results demonstrate the improvements. The method has been trained, validated and tested on publicly available labelled MRI datasets. Future developments and perspectives are also presented
A Novel Multimodal Framework To Support Human-Computer Interaction
No full textThe new tendency in human-computer interaction is to exploit all humans ex-
pressive forms to enable natural interaction with different applications and de-
vices. Usually, hand gesture and speech modalities represent the best to imple-
ment suitable interfaces in every application context. Developing a framework
to define and recognise any set of hand gestures (with or without physical con-
trollers) associating it to a set of vocal sentences is still challenging. In fact,
on one side, different sets of gestures are characterised by different recogni-
tion approaches derived from context, device or application needs. On the other
hand, the matching process between a specific gesture and a particular sentence
is prone to ambiguous interpretations, errors and coarse simplifications. This
paper describes a novel gesture and speech based framework to generate a set of
bi-modal interfaces designed to be plugged-in with XML compatible devices
Design of a 3D platform for immersive neurocognitive rehabilitation
No full textIn recent years, advancements in human-computer interaction (HCI) have enabled the development of versatile immersive devices, including Head-Mounted Displays (HMDs). These devices are usually used for entertainment activities as video-gaming or augmented/virtual reality applications for tourist or learning purposes. Actually, HMDs, together with the design of ad-hoc exercises, can also be used to support rehabilitation tasks, including neurocognitive rehabilitation due to strokes, traumatic brain injuries, or brain surgeries. In this paper, a tool for immersive neurocognitive rehabilitation is presented. The tool allows therapists to create and set 3D rooms to simulate home environments in which patients can perform tasks of their everyday life (e.g., find a key, set a table, do numerical exercises). The tool allows therapists to implement the different exercises on the basis of a random mechanism by which different parameters (e.g., objects position, task complexity) can change over time, thus stimulating the problem-solving skills of patients. The latter aspect plays a key role in neurocognitive rehabilitation. Experiments obtained on 35 real patients and comparative evaluations, conducted by five therapists, of the proposed tool with respect to the traditional neurocognitive rehabilitation methods highlight remarkable results in terms of motivation, acceptance, and usability as well as recovery of lost skills
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