mixture of Gaussian model
A survey on automatic speech recognition system
... The Gaussian model is a probability density ...prior model [1]. A Gaussian mixture model is a weighted sum of M component Gaussian densities, is given by the equation, ...
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Individual pig object detection algorithm based on Gaussian mixture model
... the Gaussian mixture mode background model, and used SURF feature matching algorithm to suppress the ...the Gaussian mixture model to segment color image of diseased ...used ...
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Enhanced load profiling for residential network customers
... Linear Gaussian model based load profiling techniques that compactly capture multiple behaviors exhibited by residential customers who have traditionally been assumed to be ...The mixture ...
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A Framework for Improving the Interpersonal Relationship of the Elderly with Mild Cognitive Impairment by Using Speaker Recognition and Social Network Platforms
... the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input ...
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Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
... locality, mixture-of-experts (MoE) models [8] have been applied to GP regression [14, 17, ...local model possesses its own set of hyper-parameters to be ...
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Enhancing Clustering Mechanism by Implementation of EM Algorithm for Gaussian Mixture Model
... A mixture model has been explain by assuming that every observed data point had a corresponding unobserved data point, or latent variable, specifying mixture component that each data point belongs ...
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An Emotion Recognition System based on Right Truncated Gaussian Mixture Model
... Truncated Gaussian mixture model ...a model using Right Truncated Gaussian mixture model and K-means algorithm to classify the emotion ...the Gaussian ...
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Cluster Weighted Modeling as a Basis for Fuzzy Modeling
... through the embedding of past practice and mature techniques in the general non-linear framework. Fuzzy modeling has evolved over the years for dealing with problems of dynamic systems. Recently, Generalized Fuzzy ...
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Low-dimensional representation of Gaussian mixture model supervector for language recognition
... In this article, we propose a new feature which could be used for the framework of SVM-based language recognition, by introducing the idea of total variability used in speaker recognition to language recognition. We ...
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Segmentation of multi temporal images using gaussian mixture model (GMM)
... segmentation model as well as a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown ...
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Gaussian Mixture Model based Spatial Information Concept for Image Segmentation
... c-means (FGFCM) [35], and hidden markov random field based fuzzy c-means (HMRF-FCM) [89]. The source code for the SVFMM algorithm can be down- loaded from http://www.cs.uoi.gr/∼kblekas/sw/MAPsegmentation.html. Pa- ...
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Gaussian-Induced Convolution for Graphs
... novel Gaussian-induced convo- lution network to handle with general irregular graph ...several Gaussian components and then performing different filtering operations on each Gaussian direction like ...
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Image segmentation-MR Images Segmentation with A Modified Gaussian Mixture Model
... classification model [7, 12, 6, ...the model assumes that all voxels contain only one tissue type, the voxels that contain a mixture of tissues may not be modelled ...
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Review on Automatic Fast Moving Object Detection in Video of Surveillance System
... foreground model, Gaussian formulation is used to depict the spatial correlation between targets in ...and Gaussian formulation is carried out for foreground ...
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Scale Mixture of Gaussian Modelling of Polarimetric SAR Data
... MG model in black, the ML in green, the MK in red, and the MNIG in ...each model was the best fit is depicted in red, a good fit in magenta, and a poor fit in ...each model was considered a poor fit ...
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Splitting of Gaussian Models via Adapted BML Method Pertaining to Cry Based Diagnostic System
... ture model over all training data, M k is the number of parameters used in model F k , and T denotes total number of training ...known mixture of multivariate Gaussian ...
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On Segmentation of Moving Objects by Integrating PCA Method with the Adaptive Background Model
... a Gaussian mixture model (GMM) for the back- ground subtraction ...the Gaussian mixture model ...using mixture of Gaussians for object detection ...using Gaussian ...
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Identifying mixtures of mixtures using Bayesian estimation
... We propose suitable priors for fitting an identified mixture of normal mixtures model within the Bayesian framework of model-based clustering. This approach allows for (1) automatic determi- nation ...
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Modelling the penumbra in computed tomography
... Practical users of the nls method need to be aware of two modes of failure. Firstly, the linear problem above may not have full column rank, and thus standard methods will not work. In R, this raises an error described ...
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Energy Efficiency Metaheuristic Mechanism for Cloud Broker in Multi-Cloud Computing
... react to the dynamic price model from the CSPs and the incoming VMs requests from the cloud users with prediction model based on the Gaussian Mixture Model.. Proposed Model[r] ...
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