Variational techniques for medical and image processing applications using generalized Gaussian distribution

In this thesis, we propose a novel approach that can be used in modeling non-Gaussian data using the generalized Gaussian distribution (GGD). The motivation behind this work is the shape flexibility of the GGD because of which it can be applied to model different types of data having well-known marked deviation from the Gaussian shape. We present the variational expectation-maximization algorithm to evaluate the posterior distribution and Bayes estimators of GGD mixture models. With well defined prior distributions, the lower bound of the variational objective function is constructed. We also present a variational learning framework for the infinite generalized Gaussian mixture (IGGM) to address the model selection problem; i.e., determination of the number of clusters without recourse to the classical selection criteria such that the number of mixture components increases automatically to best model available data accordingly. We incorporate feature selection to consider the features that are most appropriate in constructing an approximate model in terms of clustering accuracy. We finally integrate the Pitman-Yor process into our proposed model for an infinite extension that leads to better performance in the task of background subtraction. Experimental results show the effectiveness of the proposed algorithms

Modeling of Subband Image Data for Buffer Control

4nonenoneCALVAGNO G.; GHIRARDI G.; MIAN G.A.; RINALDO RCalvagno, G.; Ghirardi, G.; Mian, G. A.; Rinaldo, Robert

Modeling of subband image data for buffer control

In this work we develop an adaptive scheme for quantization of subband or transform coded frames in a typical video sequence coder, Using a generalized Gaussian model for the subband or transform coefficients, we present a procedure to determine the optimum dead-zone quantizer for a given entropy of the quantizer output symbols, We End that, at low bit rates, the dead-zone quantizer offers better performance than the uniform quantizer. The model is used to develop an adaptive procedure to update the quantizer parameters on the basis of the state of a channel buffer with constant output rate and variable input rate. We compare the accuracy of the generalized Gaussian model in predicting the actual bit rate to that achievable using the simpler and more common Laplacian model, Experimental results show that the generalized Gaussian model has superior performance than the Laplacian model, and that it can be effectively used in a practical scheme for buffer control