Proceedings of the 6th international conference on disability, virtual reality and associated technologies (ICDVRAT 2006)

The proceedings of the conference

Proceedings of the 5th international conference on disability, virtual reality and associated technologies (ICDVRAT 2004)

The proceedings of the conference

Special Issue on Virtual Reality Technologies for Disability and Rehabilitation

The papers presented in this issue provide a glimpse of the International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT) research community, illustrating advances in virtual reality and associated technologies facilitating interaction in physical and digital environments for individuals and practitioners in disability and rehabilitation. We hope that you will find this issue of interest and recommend this journal and use it to communicate this research to a broader public.

A decade of research and development in disability, virtual reality and associated technologies: review of ICDVRAT 1996−2006

The International Conference (series) on Disability, Virtual Reality and Associated Technologies (ICDVRAT) this year held its sixth biennial conference, celebrating ten years of research and development in this field. A total of 220 papers have been presented at the first six conferences, addressing potential, development, exploration and examination of how these technologies can be applied in disabilities research and practice. The research community is broad and multi-disciplined, comprising a variety of scientific and medical researchers, rehabilitation therapists, educators and practitioners. Likewise, technologies, their applications and target user populations are also broad, ranging from sensors positioned on real world objects to fully immersive interactive simulated environments. A common factor is the desire to identify what the technologies have to offer and how they can provide added value to existing methods of assessment, rehabilitation and support for individuals with disabilities. This paper presents a brief review of the first decade of research and development in the ICDVRAT community, defining technologies, applications and target user populations served.

The effects of web logs and the semantic web on autonomous web agents

Search engines exploit the Web's hyperlink structure to help infer information content. The new phenomenon of personal Web logs, or 'blogs', encourage more extensive annotation of Web content. If their resulting link structures bias the Web crawling applications that search engines depend upon, there are implications for another form of annotation rapidly on the rise, the Semantic Web. We conducted a Web crawl of 160 000 pages in which the link structure of the Web is compared with that of several thousand blogs. Results show that the two link structures are significantly different. We analyse the differences and infer the likely effect upon the performance of existing and future Web agents. The Semantic Web offers new opportunities to navigate the Web, but Web agents should be designed to take advantage of the emerging link structures, or their effectiveness will diminish.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.