Much research on 3D graphics collaboration has focused on improving collaborative viewing or enabling simple manipulations. There are, however, two major problems which are not yet resolved in collaborative environments. Simultaneous manipulation of a common 3D object by users can cause conflicts, and the integrity of the source 3D object will be compromised during updates. 3D objects can be locked for exclusive use to prevent conflicts, but this requires users to wait for their turn in order to perform manipulations. The capability of seamless and simultaneous manipulation raises several challenges such as how to resolve manipulation conflict and to deliver manipulation commands to users.

Information uncertainty is inherent in many problems and is often subtle and complicated to understand. Although visualization is a powerful means for exploring and understanding information, information uncertainty visualization is ad hoc and not widespread. This paper identifies two main barriers to the uptake of information uncertainty visualization: firstly, the difficulty of modeling and propagating the uncertainty information; and secondly, the difficulty of mapping uncertainty to visual elements. To overcome these barriers, we extend the spreadsheet paradigm to encapsulate uncertainty details within cells. This creates an inherent awareness of the uncertainty associated with each variable. The spreadsheet can hide the uncertainty details, enabling the user to think simply in terms of variables. Furthermore, the system can aid with automated propagation of uncertainty information, since it is intrinsically aware of the uncertainty. The system also enables mapping the encapsulated uncertainty to visual elements via the formula language and a visualization sheet. Support for such low-level visual mapping provides flexibility to explore new techniques for information uncertainty visualization.

In this demonstration, we will show how smartphones can be used as a platform for monitoring environmental change, particularly with respect to birdlife. We have researched and implemented a platform using Microsoft smartphones for remotely monitoring birds. The platform comprises smart-phones running a custom application for recording bird song. The application manages sensors in an autonomic fashion to ensure that they operate reliably for long periods of time in a power efficient manner.

Human emotional behavior, personality, and body language are the essential elements in the recognition of a believable synthetic story character. This paper presents an approach using story scripts and action descriptions in a form similar to the content description of storyboards to predict specific personality and emotional states. By adopting the Abridged Big Five Circumplex (AB5C) Model of personality from the study of psychology as a basis for a computational model, we construct a hierarchical fuzzy rule-based system to facilitate the personality and emotion control of the body language of a dynamic story character. The story character can consistently perform specific postures and gestures based on his/her personality type. Story designers can devise a story context in the form of our story interface which predictably motivates personality and emotion values to drive the appropriate movements of the story characters. Our system takes advantage of relevant knowledge described by psychologists and researchers of storytelling, nonverbal communication, and human movement. Our ultimate goal is to facilitate the high-level control of a synthetic character.

Geographic Information System (GIS) technology has many applications and plays a vital role in the majority of the daily operations by government and public administration. However, due to its technical complexity and cost, communities lacking the expertise and resources cannot benefit from this technology. The OpenCIS project, a user‐friendly free Open Source GIS‐based Web Community Information System, seeks to address this issue by bridging the gap between a simple map viewer and full GIS, representing one of the first steps towards the goal of grassroots empowerment through GIS technology.

To sustain an ongoing rapid growth of video information, there is an emerging demand for a sophisticated content-based video indexing system. However, current video indexing solutions are still immature and lack of any standard. This doctoral consists of a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple audio-visual modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s).

For some time now, image retrieval approaches have been developed that use low-level features, such as colour histograms, edge distributions and texture measures. What has been lacking in image retrieval approaches is the development of general methods for more structured object recognition. This paper describes in detail a general hierarchical image classifier approach, and illustrates the ease with which it can be trained to find objects in a scene. To further illustrate the wide capabilities of this approach, results from its application to particle picking in biology and Vietnamese art image retrieval are listed.

An effective scheme for soccer summarization is significant to improve the usage of this massively growing video data. The work presents an extension to our recent work which proposed a framework to integrate highlights into play-breaks to construct more complete soccer summaries. The current focus is to demonstrate the benefits of detecting some specific audio-visual features during play-break sequences in order to classify highlights contained within them. The main purpose is to generate summaries which are self-consumable individually. To support this framework, the algorithms for shot classification and detection of near-goal and slow-motion replay scenes is described. The results of our experiment using 5 soccer videos (20 minutes each) show the performance and reliability of our framework.

Existing geographic information systems (GIS) are intended for expert users and consequently, do not provide any machine intelligence to assist users. This paper presents a Bayesian framework that will incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query. The framework uses a spatial model that combines relational, non-spatial and spatial data. This spatial model allows efficient access of relational linkages for a Bayesian network, and thus improves support for complex and vague queries. The Bayesian network assigns causal probabilities to these relational linkages in order to define expert knowledge of related datasets in the GIS. In addition, the framework will learn which datasets are best suited for particular query input through feedback supplied by the user. This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis. The initial user query can be vague and the framework will still be capable of retrieving relevant datasets via the linkages discovered in the Bayesian network.

Content-based image retrieval (CBIR) has been investigated extensively in the past decade in order to classify and search images according to similarities derived from automatically extracted visual features, such as colours, textures and object shapes. It has now been realised that two fundamental problems in CBIR, namely, feature extraction and similarity measure, are likely to be domain specific. In this paper, we present some early results of applying CBIR to traditional Chinese paintings. Our research is motivated by three main goals: (1) to develop tools for art historians to study evolution and cross-influences of oriental paintings by automatically identifying visual artistic clues from digitised paintings; (2) to verify and further advance the existing CBIR techniques by limiting the images studied to a specific and simpler domain of traditional Chinese paintings; and (3) to verify and further advance the problem of high-dimensional data clustering (especially in relation to the "dimensional curse" problem). We present a framework for modelling traditional Chinese paintings, and examine various existing CBIR proposals and algorithms for their suitability for traditional Chinese paintings. In this paper, we also present a research agenda to study the problems of Chinese paintings classification and retrieval based on the framework.

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.

Present fuzzy system visualisations while being useful, have been ad hoc in nature. In this paper we present a new integrated task-oriented approach to the visualisation of fuzzy logic systems. In addition, this approach is incorporated into an agent-based software architecture to guide the user through the process of developing a fuzzy logic system visualisation. A case study then illustrates a structured walk through of the new approach with an electricity spot price prediction application.

Experiments have shown that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these regions of interest contain low level feature differences that influence the fixation time of the viewer. We present a fuzzy logic model of visual attention which seeks to compute the relative visual importance of regions in an image based upon these spatial feature differences. We also demonstrate some of the possible savings to be had in applying the visual importance model to the modulation of super-sampling In a ray-traced image. We expect this approach to have applications in the entertainment industry where image fidelity may be sacrificed for efficiency purposes.

The paper studies the problem of information uncertainty evaluation in modern engineering and technology applications, and especially the system design. It analyses the virtual environment design and engineering measurement. Information typical for those applications is classified according to its uncertainty types. Uncertainty sources are identified. Fuzzy theory models are proposed. Examples of their applications in characteristic problems are given.

Many decisions need to be made based on imprecise or incomplete initial information. In such cases, decision makers are generally more interested in sets of the most promising solutions rather than the best single solution. Therefore, in contrast to conventional optimisation approaches that aim to find exact optimal points, we aim to find optimal ranges with variable satisfaction degrees. The paper presents a fuzzy-set-based approach for the representation and optimisation of practical problems with imprecise properties where evolutionary computation is used for obtaining fuzzy solutions through guided searching. The representation of fuzzy sets, its initialisation, crossover, mutation, and validation, the ranking approach for fuzzy objective values, and the propagation method of fuzzy information are discussed. Several examples for illustrating the fuzzy evolutionary optimisation approach are provided.

Alternative models that use B-spline curves and surfaces for generating color sequences for univariate, bivariate, and trivariate mapping are introduced. The main aim is to break away from simple geometric representation in order to provide more flexibility and control over color selection. This facilitates the task of constructing a customized color scheme for a particular map. The author gives a brief description of existing color schemes and their characteristics, and provides some background for B-spline curves and surfaces.>

To allow more flexibility for shape control in curve fitting, polynomial Beta-splines were introduced as an extension of polynomial B-splines with bias and tension parameters. It is shown that a segment of a cubic Beta-spline with distinct values for bias and tension parameter at knots can be transformed into a cubic B-spline segment if the control vertices are suitably modified. This gives rise to simple algorithms for calculating Beta-splines. These algorithms compare favourably to three other algorithms given by Goodman/Unsworth, Farin/Boehm and Joe.

Polynomial Beta-splines were introduced by Barsky as an extension of polynomial B-splines with bias and tension parameters which allow more flexibility in controlling shape in curve fitting. It is possible to show that a quadratic Beta-spline segment is equivalent to a quadratic B-spline segment with suitably modified control vertices. This provides a simple method for evaluating quadratic Beta-splines using De Boor's algorithm for calculating polynomial B-splines. A representation for conic Beta-splines with one tension parameter is introduced and some properties are derived. They form a basis for an efficient algorithm for interactive curve fitting with conic Beta-splines. The results are extended further to cover the case of conic Beta-splines with varying tension where the tension parameter is an interpolating function between the tension values at each end of a segment.