Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Analysis of the compressive behaviour of the three-dimensional printed porous titanium for dental implants using a modified cellular solid model

A set of cylindrical porous titanium test samples were produced using the three-dimensional printing and sintering method with samples sintered at 900 °C, 1000 °C, 1100 °C, 1200 °C or 1300 °C. Following compression testing, it was apparent that the stress-strain curves were similar in shape to the curves that represent cellular solids. This is despite a relative density twice as high as what is considered the threshold for defining a cellular solid. As final sintering temperature increased, the compressive behaviour developed from being elastic-brittle to elastic-plastic and while Young's modulus remained fairly constant in the region of 1.5 GPa, there was a corresponding increase in 0.2% proof stress of approximately 40-80 MPa. The cellular solid model consists of two equations that predict Young's modulus and yield or proof stress. By fitting to experimental data and consideration of porous morphology, appropriate changes to the geometry constants allow modification of the current models to predict with better accuracy the behaviour of porous materials with higher relative densities (lower porosity).