Neonatal facial and cry responses to invasive and non-invasive procedures

Evaluation of pain in neonates is difficult due to their limited means of communication. The aim was to determine whether behavioural reactions of cry and facial activity provoked by an invasive procedure could be discriminated from responses to non-invasive tactile events. Thirty-six healthy full-term infants (mean age 2.2 h) received 3 procedures in counterbalanced order: intramuscular injection, application of triple dye to the umbilical stub, and rubbing thigh with alcohol. Significant effects of procedure were found for total face activity and latency to face movement. A cluster of facial actions comprised of brow bulging, eyes squeezed shut, deepening of the naso-labial furrow and open mouth was associated most frequently with the invasive procedure. Comparisons between the 2 non-invasive procedures showed more facial activity to thigh swabbing and least to application of triple dye to the umbilical cord. Acoustic analysis of cry showed statistically significant differences across procedures only for latency to cry and cry duration for the group as a whole. However, babies who cried to two procedures showed higher pitch and greater intensity to the injection. There were no significant differences in melody, dysphonation, or jitter. Methodological difficulties for investigators in this area were examined, including criteria for the selection of cries for analysis, and the logical and statistical challenges of contrasting cries induced by different conditions when some babies do not always cry. It was concluded that facial expression, in combination with short latency to onset of cry and long duration of first cry cycle typifies reaction to acute invasive procedures.

Fast Convergence of Regularised Region-based Mixture of Gaussians for Dynamic Background Modelling

The momentum term has long been used in machine learning algorithms, especially back-propagation, to improve their speed of convergence. In this paper, we derive an expression to prove the O(1/k2) convergence rate of the online gradient method, with momentum type updates, when the individual gradients are constrained by a growth condition. We then apply these type of updates to video background modelling by using it in the update equations of the Region-based Mixture of Gaussians algorithm. Extensive evaluations are performed on both simulated data, as well as challenging real world scenarios with dynamic backgrounds, to show that these regularised updates help the mixtures converge faster than the conventional approach and consequently improve the algorithm’s performance.

Evidence reasoning for event inference in smart transport video surveillance

In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

PatchCity: Procedural City Generation using Texture Synthesis

PatchCity is a new approach to the procedural generation of city models. The algorithm uses texture synthesis to create a city layout in the visual style of one or more input examples. Data is provided in vector graphic form from either real or synthetic city definitions. The paper describes the PatchCity algorithm, illustrates its use, and identifies its strengths and limitations. The technique provides a greater range of features and styles of city layout than existing generative methods, thereby achieving results that are more realistic. An open source implementation of the algorithm is available.