scholarly journals Tsallis generalized entropy for Gaussian mixture model parameter estimation on brain segmentation application

2021 ◽  
pp. 100002
Author(s):  
Mehran Azimbagirad ◽  
Luiz Otavio Murta Junior
Keyword(s):  
Parameter Estimation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Brain Segmentation ◽  
Generalized Entropy ◽  
Model Parameter ◽  
Model Parameter Estimation

scholarly journals Simulation of Tennis Match Scene Classification Algorithm Based on Adaptive Gaussian Mixture Model Parameter Estimation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuwei Wang ◽  
Mofei Wen
Keyword(s):  
Parameter Estimation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Semantic Analysis ◽  
Gaussian Mixture ◽  
Tennis Ball ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Tennis Balls ◽  
Tennis Match

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model parameter estimation algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability.

scholarly journals Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1090 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Parameter Estimation ◽  
Mixture Model ◽  
Optimization Algorithm ◽  
Likelihood Estimation ◽  
Gaussian Mixture ◽  
Difficult Problem ◽  
Integer Optimization ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Histogram Estimation

A maximum-likelihood estimation of a multivariate mixture model’s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer value; i.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package.

Cost Function Based on Gaussian Mixture Model for Parameter Estimation of a Chaotic Circuit with a Hidden Attractor

2014 ◽  
Vol 24 (01) ◽  
pp. 1450010 ◽  
Author(s):  
Seng-Kin Lao ◽  
Yasser Shekofteh ◽  
Sajad Jafari ◽  
Julien Clinton Sprott
Keyword(s):  
Parameter Estimation ◽  
Cost Function ◽  
State Space ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Real System ◽  
Hidden Attractor ◽  
The Real ◽  
Likelihood Score

In this paper, we introduce a new chaotic system and its corresponding circuit. This system has a special property of having a hidden attractor. Systems with hidden attractors are newly introduced and barely investigated. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. We use a geometry-based cost function to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian Mixture Model (GMM) which is fitted to the observed attractor generated by the real system in state space. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. The results show the adequacy of the proposed cost function.

A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

2015 ◽  
Vol 20 (2) ◽  
pp. 469-481 ◽  
Author(s):  
Yasser Shekofteh ◽  
Sajad Jafari ◽  
Julien Clinton Sprott ◽  
S. Mohammad Reza Hashemi Golpayegani ◽  
Farshad Almasganj
Keyword(s):  
Parameter Estimation ◽  
Cost Function ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Biological Systems ◽  
Gaussian Mixture ◽  
Model Based
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