Jolon Faichney

Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling

In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive handwriting recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithms performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.

Goldleaf hierarchical document browser

A two-dimensional, zoomable, space filling user interface is presented for browsing conventional, hierarchical file system. Through user studies, the Goldleaf browser was compared with the widely used Microsoft Windows Explorer user interface. The times and number of mouse clicks to locate directories and files were recorded. The user studies found that the Goldleaf browser required less than half the mouse clicks to locate a directory compared with Windows Explorer. Through the use of document thumbnails, subjects were able to locate documents in less than two-thirds the time that it took using Windows Explorer. A majority of subjects felt that the ability of the Goldleaf browser to display multiple levels of the file system simultaneously was its most beneficial feature in completing the tasks. Subjects found that the Goldleaf browser required less mental and physical effort and was more enjoyable to use than Explorer.

Video Coding for Mobile Handheld Conferencing

Since the time of Dick Tracy cartoons, the idea of wearable videoconferencing devices has been a desirable but unachievable goal. Current technology is now on the verge of making this dream a reality. Existing and future low bit-rate video coding standards such as H.263 and MPEG-4 may require specialized hardware for real-time handheld video conferencing [3]. This paper evaluates the performance of software-based videoconferencing on widely available mobile handheld devices using a range of both traditional and original video coding schemes. Our results demonstrate that wearable videoconferencing can no longer be relegated to the realm of science fiction as practical first generation devices are feasible today.

Combined colour and contour representation using anti-aliased histograms

Image databases require accurate colour image retrieval, minimal storage requirements, and fast query execution. Histograms have been used to compactly represent colour distributions providing low storage requirements and fast query execution. We propose the novel idea to also use histograms to represent contour distributions. Our contour distribution approach is shown to perform as well as an exhaustive contour comparison approach. In addition, anti-aliased histograms also known as fuzzy histograms are applied to reduce the storage requirements and query times by a factor of four. Colour and contour similarities are combined to provide more accurate query results than either individual technique.

Software video coding for handheld conferencing

Since the time of Dick Tracy cartoons, the idea of wearable videoconferencing devices have been a desirable but unachievable goal. Current technology is now on the verge of making this dream a reality. Existing and future low bit-rate video coding standards such as H.263 and MPEG-4 may require specialised hardware for real-time handheld video conferencing. This paper evaluates the performance of software-based videoconferencing on widely available mobile handheld devices using a range of both traditional and original video coding schemes. Our results demonstrate that wearable videoconferencing can no longer be relegated to the realm of science fiction as practical first generation devices are feasible today.

Bio-Cell Image Segmentation Using Bayes Graph-Cut Model

The accurate segmentation of biomedical images has become increasingly important for recognizing cells that have the phenotype of interest in biomedical applications. In order to improve the conventional deterministic segmentation models, this paper proposes a novel graph-cut cell image segmentation algorithm based on Bayes theorem. There are two segmentation phases in this method. The first phase is an interactive process to specify a preliminary set of regional pixels and the background based on the interactive graph-cut model. In the second phase, final segmentation is calculated based on the idea of Bayes theorem, combining prior information with data. Our idea can be considered an integration of graph-cut methods and Bayes theorem for cell image segmentation. Experimental results show that the proposed model performs better in comparison with several existing methods.

Multi-Directional Weighted Interpolation for Wi-Fi Localisation

The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limitations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi localisation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final precision, and the time and money spent training the system. We propose Multi-Directional Weighted Interpolation (MDWI), a probabilistic-based weighting mechanism to predict unseen locations. Our results show that MDWI uses half the number of training points whilst increasing accuracy by up to 24%.