A simulation model for video traffic performance via ATM over TCP/IP

Although TCP has emerged as the standard in data communication, the introduction of ATM technology has raised numerous problems regarding the effectiveness of using TCP over A TM networks, especially when video traffic performance is considered. This paper presents a simulation model for transmission performance of video traffic via ATM over TCP/IP. The interactivity between TCP/IP and ATM, generation of MPEG traffic and evaluation of traffic performance are implemented in the model. The design and implementation details of the model are carefully described. The experiments conducted using the model and experimental results are briefly introduced, revealing the capability of our model in simulating network events and in evaluating potential solutions to performance issues.

Reducing blocking artefacts in MPEG video coded images using entropic methods

In this paper investigation is made of the. use of entropic methods to identify smooth image blocks in order to filter the reconstructed image of block based transform coded images. We know that blocking artefact is Visually more prominent in the smooth region of a block rather than in the textured region. The entropic methods are employed to distinguish between smooth and textured blocks in a reconstructed image. The linear filtering is then carried out on the smooth region of the image to reduce the blocking artefact. The en tropic criteria investigated in this paper are: Shannon's (Logarithmic) Entropy, Exponential Entropy, and Quadratic Entropy.

Recording the past to prepare for the future : the role of the teaching portfolio

This paper discusses the role of the portfolio in relation to teaching in the higher education sector. As well as evidencing experience and achievements it is argued that the portfolio can be an important tool to support reflection and increased understanding. Underpinned by relevant scholarly literature, the paper draws on the practice of using portfolios as part of the Induction Programme for new staff at Deakin University since 2001 and for promotion since 2002 as well as a trial of electronic portfolios. Commencing with indicators of the level of interest in portfolios, the author characterises teaching portfolios and explains why they are used from both an individual and institutional point of view. The theoretical basis for portfolios and the experience elsewhere are set alongside the practice at Deakin as the author explores some of the challenges for those who prepare and assess portfolios.

Classification of self-consumable highlights for soccer video summaries

An effective scheme for soccer summarization is significant to improve the usage of this massively growing video data. The paper 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.

The power of play-break for automatic detection and browsing of self-consumable sport video highlights

To enable content-based retrieval, highlights extraction from broadcasted sport video has been an active research topic in the last decade. There is a well-known theory that high-level semantic, such as goal in soccer can be detected based on the occurrences of specific audio and visual features that can be extracted automatically. However, there is yet a definitive solution for the scope (i.e. start and end) of the detection for self consumable highlights. Thus, in this paper we will primarily demonstrate the benefits of using play-break for this purpose. Moreover, we also propose a browsing scheme that is based on integrated play-break and highlights (extended from [1]). To validate our approach, we will present the results from some experiments and a user study.

Performance of statistical inference for block classification in reducing blocking artefact in video coded images

This paper presents a performance study of four statistical test algorithms used to identify smooth image blocks in order to filter the reconstructed image of a video coded image. The four algorithms considered are the Coefficient of Variation (CV), Exponential Entropy of Pal and Pal (E), Shannon's (Logarithmic) Entropy (H), and Quadratic Entropy (Q). These statistical algorithms are employed to distinguish between smooth and textured blocks in a reconstructed image. The linear filtering is carried out on the smooth blocks of the image to reduce the blocking artefact. The rationale behind applying the filter on the smooth blocks only is that the blocking artefact is visually more prominent in the smooth region of an image rather than in the textured region.

Video lectures for cross-cultural use: a three phase model

This paper will discuss the contributing factors of; increasing numbers of international students, and the advancement of learning technologies; that lead to the development of an exploratory research study into the creation of a three phase online lecture model. Aspects such as the differences between the major cultural groups currently enrolled in Australian Universities, indicates the variations that can be expected in student learning styles. This research study aims at determining the educational value of the inclusion of online lectures for a diverse and distributed cross-cultural audience.

Content-based video indexing for sports applications using integrated multi-modal approach

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).

Video games research: gender inequities

In this paper we explore current research on violence in video games, with a focus on gender inequalities and imbalance, and introduce a project being undertaken as part of an honours programme in Information Technology. The research focuses on the portrayal of violence in games as perceived by future game developers and the parallel issue of lack of female participation as players and developers. Although the project is in its infancy it has already highlighted a major shortcoming in video games research which will have a major impact on the results. Lack of gender representation in the population being surveyed limits the generality of any results obtained, which has a domino effect on the ability of the games industry to address gender issues.

Robust video/ultrasonic fusion based estimation for automotive applications

We describe how object estimation by a stationary or a non-stationary camera can be improved using recently-developed robust estimation ideas. The robustness of vision-based systems can be improved significantly by employing a Robust Extended Kalman Filter (REKF). The system performance is also enhanced by increasing the spatial diveristy in measurements via employing additional cameras for video capture. We describe a normal-flow based image segmentation technique to identify the object for the application of our proposed state estimation technique. Our simulations demonstrate that dynamic system modelling coupled with the application of a REKF significantly improves the estimation system performance, especially when large uncertainties are present.

Marine benthic habitat mapping using multibeam data, georeferenced video and image classification techniques in Victoria, Australia

This paper contributes to a better understanding of geophysical characteristics and benthic communities in the Hopkins site in Victoria, Australia. An automated decision tree classification system was used to classify substrata and dominant biota communities. Geophysical sampling and underwater video data collected in this study reveals a complex bathymetry and biological structure which complements the limited information of benthic marine ecosystems in coastal waters of Victoria. The technique of combining derivative products from the backscatter and the bathymetry datasets was found to improve separability for broad biota and substrata categories over the use of either of these datasets alone.

Robust video/ultrasonic fusion-based estimation for automotive applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.

A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.