Brain image segmentation is an important part of medical image analysis. Due to the effect of imaging mechanism, MR images usually intensity in homogeneity, which is also named as bias field. Traditional Gaussian Mixed Model (GMM) method is hard to obtain satisfied segmentation results with the effect of noise and bias field. We propose a novel model based on GMM and nonlocal information. The i...

Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap to some extent. In the first and second...

Empirical researchers frequently use dynamic panel data models and employ Generalized Method of Moments (GMM) estimators (Hansen, 1982) to estimate model parameters. An important practical problem in the estimation of a dynamic panel data model is the choice of moments as it provides a large number of moment conditions. Even though adding moment conditions leads to efficiency gain according to ...

The background modeling algorithm based on Gaussian mixture model (GMM) is a widely used method in moving objects detection with static cameras. Base on the situation that traditional Gaussian mixture model is very sensitive to sudden illumination variation and is slow for convergence speed, this paper proposed a method to detect the illumination variation and update the single learning rate, i...

Spectral unmixing given a library of endmember spectra can be achieved by multiple endmember spectral mixture analysis (MESMA), which tries to find the optimal combination of endmember spectra for each pixel by iteratively examining each endmember combination. However, as library size grows, computational complexity increases which often necessitates a laborious and heuristic library reduction ...

For noise-robust automatic speech recognition (ASR), we propose a novel voice activity detection (VAD) method based on a combination of multiple features. The scheme uses a weighted combination of four conventional VAD features: amplitude level, zero crossing rate, spectral information, and Gaussian mixture model (GMM) likelihood. The weights for combination are adaptively updated using minimum...

A new method of text-independent speaker recognition using discriminative feature selection is proposed in this paper. The method is compared with the classical UBM-GMM and hybrid codebook approaches. The characteristics of the proposed method are as follows: feature parameters extraction, vector quantization with the growing neural gas (GNG) algorithm and discriminative feature selection (DFS)...

A new method to smooth the target hybrid state with Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) in the presence of clutter for a high pulse repetition frequency (HPRF) radar is proposed. This method smooths the target state at fixed lag N and considers all feasible multi-scan target existence sequences in the temporal window of scans in order to smooth the targe...

This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCC vectors and formant vectors using a Gaussian mixture model (GMM). Using this GMM and an input MFCC vector, two maximum a posteriori (MAP) prediction methods are developed. The first method pred...

In this paper we propose a class of efficient Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for t...