Gaussian Mixture Models and Relaxation Labeling for Online Evaluation of Training in Virtual Reality Simulators
نویسندگان
چکیده
1 Ronei Marcos de Moraes, Statistics Department, Federal University of Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB Brazil, tel.: +55 83 216-7075, [email protected] 2 Liliane dos Santos Machado, Laboratory of Integrated Systems Polytechnic School University of São Paulo, Av. Prof. Luciano Gualberto, 158. Trav.3. CEP: 05508-9010 São Paulo SP Brazil, tel.: +55 11 3818-5676, [email protected] Abstract Several approaches for evaluation of online or offline training in simulators based on virtual reality have been proposed. However great part of these approaches has a high complexity and it demands large computational structure, what is very expensive. An online evaluator must have low complexity algorithm to do not compromise the performance of simulator. We propose a new approach to online evaluation of training in simulators based on virtual reality. This approach uses Gaussian Mixture Models and Relaxation Labeling (GMM-RL) for modeling and classification of the simulation in pre-defined classes of training. This method provides the use of continuous variables without lost of information. So, it solves the problem of low complexity in online evaluators without compromise performance of the simulator and with good evaluation accuracy. Systems based on this approach can be applied in virtual reality simulators for training in several areas.
منابع مشابه
Online training evaluation in VR simulators using Gaussian Mixture Models.
A new approach to evaluate training in simulators based on virtual reality is proposed. This approach uses Gaussian Mixture Models (GMM) for modeling and classification of the simulation in pre-defined classes of training.
متن کاملGaussian Naive Bayes for Online Training Assessment in Virtual Reality-Based Simulators
Training systems based on virtual reality are used in several areas, as in the medical sciences. In these systems the user is immersed into a virtual world to have realistic training through realistic interactions. In such training is important to know the quality of user's training and by didactic reasons the user must receive his/her assessment immediately after of end of training. For this r...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملOnline Assessment of Training in Virtual Reality Simulators Based on General Bayesian Networks
1 Ronei Marcos de Moraes, Department of Statistics, Universidade Federal da Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB Brazil, [email protected] 2 Liliane dos Santos Machado, Department of Informatics, Universidade Federal da Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB Brazil, [email protected] 3 Leandro Carlos de Souza, Department of Informatics, Un...
متن کاملIMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کامل