Daniel E. Riedel

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Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by(More)
In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accomodate(More)
Correlation filtering has recently been reintroduced to the facial recognition domain with promising recognition rates being seen over a variety of different facial databases. In this paper we utilise the minimum average correlation energy (MACE) and unconstrained minimum average correlation energy (UMACE) filters in conjunction with two correlation plane(More)
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