Phil L. Palmer

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We present a Kalman tracking algorithm that can track a number of very small, low contrast objects through an image sequence taken from a static camera. The issues that we have addressed to achieve this are twofold. Firstly, the detection of small objects comprising a few pixels only, moving slowly in the image, and secondly, tracking of multiple small(More)
We present a paradigm for feedback strategies that find instances of a generic class of objects by improving on established single-pass hypothesis generation and verification approaches. We improve upon the mechanisms of the traditional or classical image processing systems by introducing control strategies at low, intermediate, and high levels of analysis.(More)
We develop a paradigm for feedback strategies that combine low-level features to produce a focus of attention mechanism, and a high-level object model to direct search for missing information. The aim of this complex feedback strategy is to nd instances of a generic class of objects by improving on established single-pass hypothesis generation and(More)
The hypothesis veriication stage of the traditional image processing approach, consisting of low, medium, and high level processing , will suuer if the set of low level features extracted are of poor quality. We i n vestigate the optimisation of the feature extraction chain by using Genetic Algorithms. The tness function is a performance measure which(More)