Autonomous Secondary Gaze Behaviours

In this paper we describe secondary behaviour, this is behaviour that is generated autonomously for an avatar. The user will control various aspects of the avatars behaviour but a truly expressive avatar must produce more complex behaviour than a user could specify in real time. Secondary behaviour provides some of this expressive behaviour autonomously. However, though it is produced autonomously it must produce behaviour that is appropriate to the actions that the user is controlling (the primary behaviour) and it must produce behaviour that corresponds to what the user wants. We describe an architecture which achieves these to aims by tagging the primary behaviour with messages to be sent to the secondary behaviour and by allowing the user to design various aspects of the secondary behaviour before starting to use the avatar. We have implemented this general architecture in a system which adds gaze behaviour to user designed actions

Integrating internal behavioural models with external expression

Users will believe in a virtual character more if they can empathise with it and understand what ‘makes it tick’. This will be helped by making the motivations of the character, and other processes that go towards creating its behaviour, clear to the user. This paper proposes that this can be achieved by linking the behavioural or cognitive system of the character to expressive behaviour. This idea is discussed in general and then demonstrated with an implementation that links a simulation of perception to the animation of a character’s eyes

Summer of Code: Assisting Distance-Learning Students with Open-Ended Programming Tasks

A significant difficulty in teaching programming lies in the transition from novice to intermediate programmer, characterised by the assimilation and use of schemas of standard programming approaches. A significant factor assisting this transition is practice with tasks which develop this schema use. We describe the Summer of Code, a two-week activity for part-time, distance-learning students which gave them some additional programming practice. We analysed their submissions, forum postings, and results of a terminal survey. We found learners were keen to share and discuss their solutions and persevered with individual problems and the challenge overall. 93% respondents rated the activity 3 or better on a 5-point Likert scale (n=58). However, a quarter of participants, mainly those who described themselves as average or poor programmers, felt less confident in their abilities after the activity, though half of these students liked the activity overall. 54% of all participants said the greatest challenge was developing a general approach to the problems, such as selecting appropriate data structures. This is corroborated by forum comments, where students greatly appreciated “think aloud” presentations by faculty tackling the problems. These results strongly suggest that students would benefit from more open-ended practice, where they have to select and design their own solutions to a range of problems

Management of Widespread Pain and Fibromyalgia

Peer reviewedPublisher PD

Recovering Dense Tissue Multispectral Signal from in vivo RGB Images

Hyperspectral/multispectral imaging (HSI/MSI) contains rich information clinical applications, such as 1) narrow band imaging for vascular visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and clinical decision making [1]; 3) tissue classification and identification of pathology [2]. The current systems which provide pixel-level HSI/MSI signal can be generally divided into two types: spatial scanning and spectral scanning. However, the trade-off between spatial/spectral resolution, the acquisition time, and the hardware complexity hampers implementation in real-world applications, especially intra-operatively. Acquiring high resolution images in real-time is important for HSI/MSI in intra-operative imaging, to alleviate the side effect caused by breathing, heartbeat, and other sources of motion. Therefore, we developed an algorithm to recover a pixel-level MSI stack using only the captured snapshot RGB images from a normal camera. We refer to this technique as "super-spectral-resolution". The proposed method enables recovery of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per second (FPS) on a GPU. Multispectral data captured from porcine bowel and sheep/rabbit uteri in vivo has been used for training, and the algorithm has been validated using unseen in vivo animal experiments.Comment: accepted by Hamlyn Symposium 201