Catálogo de publicaciones - libros

Haptic perception refers to the human ability to perceive spatial properties through tactile and haptic sensations. Humans have an uncanny ability to analyze objects based only on sparse information from haptic stimuli. Contextual clues about material of an object, its overall shape, size and weight configurations perceived by individuals, lead to recognition of an object and its spatial features. In this paper, we present strategies and algorithms to model context in haptic applications that allow user to explore objects in virtual reality/augmented reality, haptically. Our methodology is based on modeling user’s cognitive and motor strategy of haptic exploration. Additionally we also model physiological arrangement of tactile sensors in the human hand. These models provide the context to adapt haptic displays to a user’s style of haptic perception and exploration and the present state of the user’s exploration. We designed a tactile cueing paradigm to test the validity of the contextual models. Initial results show improvement in accuracy and efficiency of haptic perception when compared to the conventional approaches that do not model context in haptic rendering.

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.

This paper introduces a Generic Language for Interface DEvelopment (GLIDE) that supports the rapid prototyping of Graphical User Interfaces for the Agents Channeling ContExt Sensitive Services (ACCESS) architecture. ACCESS is an open agent-based framework that enables the fabrication and deployment of context aware applications. GUIs developed for ACCESS are contained within dynamic composite objects that are composed of a number of interacting components. Scripts written in GLIDE define the structure of the GUI’s composite object and abstract out inter-component dependencies, thus ensuring that the GUI constituents remain modular, extensible and reusable. The GLIDE framework acts as a semantic model of the interface and enables the system to dynamically reconfigure and reorganize itself at run-time so as to adapt to user interaction. Additionally, GLIDE provides a mechanism to ensure that the abstract composition of the interface remains consistent across a range of ubiquitous devices.

This work proposes an approach for managing multimedia information associated to 3D environments navigated by the user. The influence of context (location, user profile, user history, time, device and network) on such management is considered in different stages of the multimedia information lifecycle, with a particular reference to the authoring and presentation phases. The different types of contexts will be modeled (i.e., with a particular emphasis on location and user history) and analyzed in relation to content creation, leading both to the definition of a hierarchy of contexts and to a data structure modeled on authoring needs. Such formalizations will be then used as key components of an implementation architecture for controlling and proactively managing multimedia information in the presentation phase. Application examples related to cultural heritage are presented, showing how such architecture can manage complex user experiences on the basis of changes of context.

Automated visual surveillance has emerged as a trendy application domain in recent years. Many approaches have been developed on video processing and understanding. Content-based access to surveillance video has become a challenging research area. The results of a considerable amount of work dealing with automated access to visual surveillance have appeared in the literature. However, the event models and the content-based querying and retrieval components have significant gaps remaining unfilled. To narrow these gaps, we propose a database model for querying surveillance videos by integrating semantic and low-level features. In this paper, the initial design of the database model, the query types, and the specifications of its query language are presented.

This paper presents an effective overlay H.263+ video multicast system over the Internet. The proposed system consists of overlay multicast tree construction, target bandwidth determining process, and H.263+ rate control. Overlay multicast tree construction algorithm and target bandwidth determining process work alternatively to satisfy the average delay constraint, and H.263+ rate control is implemented to enhance the human visual perceptual quality over the multicast tree. Finally, experimental results are provided to show the performance of the proposed system.

In (HWS), a data file is divided into pages and the pages are scheduled in channels in such a way that each page appears at least once in some channel in every window of size . The optimal HWS problem aims to maximize . Let be the largest integer satisfying ≤ , where is the harmonic number. Then is an upper bound on , if the HWS framework is used. Thus an optimal HWS schedule wastes “bandwidth” at least –. (HBWS) generalizes HWS by grouping consecutive pages into a superpage. Let be the total number of pages scheduled. The ratio / is directly proportional to the maximum initial waiting time in Media-on-Demand applications. We propose a method that starts with a HWS schedule and modifies it to generate a HBWS schedule that achieves a higher ratio /. For up to five channels, we demonstrate that we can always achieve / >. We also prove that as we increase , / approaches the theoretical upper bound.

The development of video technology and video-related applications demands strong support in semantic data models. To meet such a requirement, many video semantic data models have been proposed. The semantic model plays a key role in providing query capability and other features for a video database. However, to our knowledge, the criteria for a good semantic model remain open at present. As a result, people lack the rules for evaluating an existing model and the guidelines for the design of a new data model when necessary. To address this issue, this paper proposes twenty one properties as the criteria for video semantic models, and gives the evaluation result of eleven existing rich semantic models according to these properties. It shows that these models mostly concentrate on basic expressive power and query capability, and fulfill users’ primary requirements. But in some advanced features such as expressive power, acquisition and analysis of semantic information, and query capability etc., there are rooms for further enhancement. The paper concludes by indicating some research directions for video semantic models.