Browsing by Author "de Jager, G"

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    Partitioned particle filtering for target tracking in video sequences
    (2004) Louw, Markus Smuts; de Jager, G; Nicolls, Frederick
    [page 9-12,17,18 are missing] A partitioned particle filtering algorithm is developed to track moving targets exhibiting complex interaction in a static environment, in a video sequence. The filter is augmented with an additional scan phase, which is a deterministic sequence which has been formulated in terms of the recursive Bayesian paradigm, and yields superior results. One partition is allocated to each target object, and a joint hypothesis is made for simultaneous location of all targets in world coordinates. The observation likelihood is calculated on a per-pixel basis, using sixteen-centered Gaussian Mixture Models trained on the available colour information for each target. Assumptions about the behaviour of each pixel allow for the improvement under certain circumstances of the basic pixel classification by smoothing, using Hidden Markov Models, again on a per-pixel basis. The tracking algorithm produces very good results, both on a complex sequence using highly identifiable targets, as well as on a simpler sequence with natural targets. In each of the scenes, all of the targets were correctly tracked for a very high percentage of the frames in which they were present, and each target loss was followed by a successful reacquisition. Two hundred basic particles were used per partition, with an additional one hundred augmented particles per partition, for the scan phase. The algorithm does not run in real-time, although with optimization this is a possibility.
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    Open Access
    Use of a general imaging model to achieve predictive autofocus in the scanning electron microscope.
    (Elsevier, 1997) Nicolls, F; de Jager, G; Sewell, B
    This work outlines the development of a general imaging model for use in autofocus, astigmatism correction, and resolution analysis. The model is based on the modulation transfer function of the system in the presence of aberrations, in particular, defocus. The signals used are related to the ratios of the Fourier transforms of images captured under different operating conditions. Methods are developed for working with these signals in a consistent manner. The model described is then applied to the problem of autofocus. A fairly general autofocus algorithm is presented and results given which reflect the predictive properties of this model. The imaging system used for the generation of results was a scanning electron microscope (SEM), although the conclusions should be valid across a far wider range of instruments. It is, however, the specific requirements of the SEM that make the generalisation presented here particularly useful. We have, therefore, confined our investigation to SEM.