Kaleidoscope configurations for reflectance measurement

Simulations of different configurations of the symmetrical tapered kaleidoscope are performed to assess their merits for measurement of BRDFs and BTFs. The relationship between optimal kaleidoscope layout, and factors such as hardware restrictions and the resolution of the required reflectance function, is derived. The effect on the measurement of the reflectance function of changing these independent variables is examined through the simulation. These experiments highlight issues affecting the measurement of BTFs using kaleidoscopes, and suggest configurations that allow sampling at regular parameter intervals. A number of other kaleidoscope architectures are explored, which offer the benefits of potentially doubling the range of directions that can be sampled, and allowing adaptive control of sample intervals.

Improving the quality of professionals’ contemporaneous notes taken during interviews about alleged child abuse

The current study explored the effectiveness of note-taking instructions to increase the completeness and accuracy of professionals’ contemporaneous written notes of child abuse interviews. Police members had their base-line note-taking assessed before receiving brief instructions focusing on layout style, abbreviations, and question codes. Participants’ note-taking was reassessed immediately post-instruction and again at follow-up, 4 weeks later. Instructions increased the number of questions that participants recorded and the information that they recorded about question structure. These increases in question information did not occur at the expense of witness response information. Instructions encouraged participants to use more abbreviations and question codes and to use a clearer layout style to differentiate questions and responses. Instructing professionals in simple note-taking strategies immediately increased the completeness and accuracy of their notes. However, electronic recordings remain the most effective method of ensuring complete and accurate interview notes.

Tomorrow’s car – for today’s people : can tilting three wheeled vehicles be a solution for the problems of today and the future?

The current automotive industry and todays car drivers are faced with every increasing challenges, not previously experienced. Climate Change, financial issues, rising fuel prices, increased traffic congestion and reduced parking space in cities are all leading to changes in consumer preferences and the requirements of modern passenger vehicles. However, despite the shift in the industry dynamics, the principal layout of a car hasn’t changed since its invention. The design of a ’conventional’ vehicle is still principally a matchbox with four wheels, one at each corner. The concept has served its purpose well for over 100 years, but such a layout is not suited to solving today’s problems. To address the range of problems faced by the industry, a number of alternative commuting vehicles have been developed. Yet the commercialization of these ‘alternative’ vehicles has yet to be successful. This is largely due failure of these vehicles to meet the changing demands of the industry and the limited understanding of consumer behaviour, motivation and attitudes. Deakin University’s Tomorrow’s Car concept tackles all of these problems. The vehicle is a novel three-wheeler cross over concept between a car and a motorbike that combines the best of both worlds. The vehicle combines the low cost, small size and ‘fun’ factor of a motorbike together with the safety, comfort and easy to drive features of a car produce a vehicle with a fuel efficiency better than either car or scooter. Intensive market research has been conducted for various major potential markets of alternative vehicles including India, China and Australia. The research analysed consumer attitudes in relation to narrow tilting vehicles, and in particular towards Deakin’s Tomorrow’s Car (TC). The study revealed that a relatively large percentage of consumers find such a concept very appealing. For the other consumers, the overall appearance and perception of safety and not the actual safety performance were found to be the most impeding factors of such vehicles. By addressing these issues and marketing the vehicle accordingly the successful commercialization of Tomorrow’s Car can be ensured.

Comparative effectiveness report : online survey tools

Purpose. A group of Australian researchers seeking an accessible online survey tool discovered to their concern that most commercially available survey tools are not actually ‘useable’ by a significant number of assistive technology users.
Method. Comparative effectiveness analysis of 11 popular survey tools. A bespoke survey tool was subsequently created to meet all accessibility guidelines and useability criteria as determined by the wide range of assistive technology users with whom the research team was working.
Results. Many survey tools claim accessibility status but this does not reflect the actual situation. Only one survey met all compliance points; however, it was limited by inflexible layout and few options for question types; some surveys proved unusable by screen reader. All surveys reviewed represented a compromise between accessibility and breadth of functionality.
Conclusion. It would appear the voices of a proportion of people living with disability are absent from the data collected by surveys, and that current accessibility guidelines, even where implemented, still fall short of assuring useable survey tools. This article describes one online solution created to successfully survey a broad population, and outlines a design approach to encompass user diversity.

Students' construction of a virtual museum for multimodal meaning-making : the case of a pilot intervention

This paper evaluates a pilot design research intervention of 12-13 year old Secondary (Middle) students in Singapore constructing a virtual museum within an English Language (EL) curriculum which seeks to engage students through the infusion of authentic, rich multimodal stimuli. 78 students from two classes designed the layout of museum galleries based on their chosen themes, sourced and created multimodal artifacts , and presented their proposals and galleries to an imagined external audience and their peers. The research design and methodology, processes and factors driving the design and implementation of the project are described. Feedback from teachers and students on the benefits and limitations of the study are elicited through semi-constructed interviews, surveys and questionnaire forms. Recommendations to overcome problems identified are proposed.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Improved fade and dissolve detection for reliable video segmentation

We present improved algorithms for automatic fade and dissolve detection in digital video analysis. We devise new two-step algorithms for fade and dissolve detection and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from early work in scene change detection which focuses on identifying the existence of a transition rather than its precise temporal extent. We evaluate our algorithms against two other commonly used methods on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer, to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser, and the others from LEDs), and triangulation is carried out using the camera's viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover, the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator's knowledge of the objects.

Detection of setting and subject information in documentary video

Interpretation of video information is a difficult task for computer vision and machine intelligence. In this paper we examine the utility of a non-image based source of information about video contents, namely the shot list, and study its use in aiding image interpretation. We show how the shot list may be analysed to produce a simple summary of the 'who and where' of a documentary or interview video. In order to detect the subject of a video we use the notion of a 'shot syntax' of a particular genre to isolate actual interview sections.

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

An investigation into the use of physical modelling for the prediction of various feature types visible from different view points

This paper describes a general purpose flexible technique which uses physical modelling techniques for determining the features of a 3D object that are visible from any predefined view. Physical modelling techniques are used to determine which of many different types of features are visible from a complete set of viewpoints. The power of this technique lies in its ability to detect and parameterise object features, regardless of object complexity. Raytracing is used to simulate the physical process by which object features are visible so that surface properties (eg specularity, transparency) as well as object boundaries can be used in the recognition process. Using this technique occluding and non-occluding edge based features are extracted using image processing techniques and then parameterised. Features caused by specularity are also extracted and qualitative descriptions for these are defined.

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.