Papers by Keyword: Mixture Of Gaussians

Abstract: This paper presents a novel approach to detect unattended and removed objects from a single fixed camera video for surveillance applications. In this paper two backgrounds rather than any tracking information are employed to detect static foregrounds. Each of the backgrounds is modeled by three Gaussian mixtures. We make a subtraction between two foregrounds extracted by the background subtraction method to initially determine the static regions. Then the region-level density analysis and texture information are combined to remove false static foreground regions caused by illumination changes and objects that are in the static-moving transitions. Finally, classification of the unattended and removed is determined by a method that compares color histograms of the static foreground zone and its extended areas in current frame. Experimental results show that our method can work well in simple scenario as well as complex environments with an unsmooth background.

Abstract: This paper presents a Non-Destructive Testing (NDT) technique, Acoustic Emission (AE) to classify pitting corrosion severity in austenitic stainless steel 304 (SS304). The corrosion severity is graded roughly into five levels based on the depth of corrosion. A number of timedomain AE parameters were extracted and used as features in our classification methods. In this work, we present practical classification techniques based on Bayesian Statistical Decision Theory, namely Maximum A Posteriori (MAP) and Maximum Likelihood (ML) classifiers. Mixture of Gaussian distributions is used as the class-conditional probability density function for the classifiers. The mixture model has several appealing attributes such as the ability to model any probability density function (pdf) with any precision and the efficiency of parameter-estimation algorithm. However, the model still suffers from model-order-selection and initialization problems which greatly limit its applications. In this work, we introduced a semi-parametric scheme for learning the mixture model which can solve the mentioned difficulties. The method was compared with conventional Feed-Forward Neural Network (FFNN) and Probabilistic Neural Network (PNN) to evaluate its performance. We found that our proposed methods gave much lower classificationerror rate and also far smaller variance of the classifiers.