A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into nonoverlapping cells, followed by extracting features based on motion, size and texture from each cell. Each feature type is independently analysed for the presence of an anomaly. Unlike most methods, a refined estimate of object motion is achieved by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown codebook, with the number of entries in the codebook selected in an online fashion. Experiments on the recently published UCSD Anomaly Detection dataset show that the proposed method obtains considerably better results than three recent approaches: MPPCA, social force, and mixture of dynamic textures (MDT). The proposed method is also several orders of magnitude faster than MDT, the next best performing method.

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For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-byblock basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The background estimation is carried out in a Markov Random Field framework, where the optimal labelling solution is computed using iterated conditional modes. The clique potentials are computed based on the combined frequency response of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate that the proposed method obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed "intervals of stable intensity" method. Further experiments on the Wallflower dataset suggest that the combination of the proposed method with a foreground segmentation algorithm results in improved foreground segmentation.

Context:Dental trauma has become an important attribute of dental public health. The primary requisite before actively dealing with such problems is to describe the extent, distribution, and associated variables with the specific condition.Aims:The aim of the present study was to assess the prevalence and distribution of traumatic dental injuries (TDI) to anterior teeth among 3 to 13 years old Chidambaram school children.Settings and Design:A cross-sectional study was conducted. Data was collected through a survey form and clinical examination.Materials and Methods:A total of 3200 school children in the age group of 3-13 years were selected from 10 schools of Chidambaram, Tamilnadu. Information concerning sex, age, cause of trauma, number of injured teeth, type of the teeth, lip competence, terminal plane relationship and the molar relationship were recorded.Statistical Analysis Used:The statistical software EPIINFO (Version 6.0) was used for statistical analysis. In the present study, P≤0.05 was considered as the level of significance.Results:The trauma prevalence in the present study was 10.13%. Children with class I type 2 and mesial step molar relationship exhibited more number of dental injuries. Enamel fracture was the most common injury recorded. Only 3.37% of the children had undergone treatment.Conclusion:The high level of dental trauma and low percentage of children with trauma seeking treatment stresses the need for increased awareness in Chidambaram population.