Bryan Gardiner

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Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behavior, which is decomposed into atomic(More)
We present a biologically motivated approach to fast feature extraction on hexagonal pixel based images using the concept of eye tremor in combination with the use of the spiral architecture and convolution of non-overlapping gradient masks. We generate seven feature maps “a-trous” that can be combined into a single complete feature map, and(More)
The area of hexagonal image representation has been explored for several decades based on the hexagonal structure's many advantages over conventional rectangular image grids. Due to existing image capture and display hardware being produced for rectangular structures only, most research to date has focused on developing an efficient software approach to(More)
Image processing tasks have traditionally involved the use of square operators on regular rectangular image lattices. For many years the concept of using hexagonal pixels for image capture has been investigated, and several advantages of such an approach have been highlighted. Therefore, we present a design procedure for hexagonal gradient operators,(More)
We present a general approach to the computation of adaptive tri-directional operators for use on hexagonal pixel-based images, based on the spiral architecture. We show that the use of Gaussian basis functions within the finite element method provides a framework for a systematic design procedure for operators that are adaptive to spiral neighbourhoods(More)
As features within an image may be present at many scales, application of feature detectors at multiple scales can improve accuracy of the detected localisation and orientation. As the scale and size of a feature detector increases, so does the computational complexity of implementation across the image domain. To address this issue we present a novel(More)
A systematic design procedure is used to develop Laplacian operators that facilitate the computation of hexagonal feature map pyramids. Our focus is the development of algorithms that can operate on hexagonal images over a range of scales. We show how scalable operators can be explicitly constructed using a Gaussian neighbourhood function. We extend this(More)