Results 171 to 180 of about 34,052 (223)

PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixtures models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by principal component analysis (PCA), the generative processes for priors,
Ziquan Liu, Lei Yu, Janet H. Hsiao, Antoni B. Chan   +3 more
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Engine Overheating Prediction with Machine Learning Using Gaussian Mixture Model (GMM)

SAE Technical Paper Series, 2022
<div class="section abstract"><div class="htmlview paragraph">The Advancement in Connected vehicles Technology in recent years has propelled the use of concepts like the Internet of Things (IoT) and big data in the automotive industry. The progressive electrification of the powertrain has led to the integration of various sensors in the ...
Shrikant Deokrishna Hiwase, PRAMOD JAGTAP, Dinesh Krishna   +2 more
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Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 2022
Motion tracking apps are used for a lot of different things, like finding traffic jams and counting the number of cars going through a traffic light. The datasets come from many places on the internet, like YouTube and public dataset archives. There are about 20 videos that are tagged with the words "traffic" and "traffic camera video" and run for 10 ...
Rika Rosnelly, Bob Subhan Riza, Linda Wahyuni, S. Edy Victor Haryanto, Annas Prasetio   +4 more
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Driver face tracking using Gaussian mixture model(GMM)

IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), 2004
For the purpose of driver fatigue or attention level surveillance based on various facial cues, a practical computer vision system would probably implement at least three major components: a face detection module to locate the face as system initialization, a face tracking module to track the face in the subsequent images, and an inference module to ...
Y. Zhu, K. Fujimura
openaire   +1 more source

Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)

Applied Mechanics and Materials, 2007
This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and
J. Sun, Gang Yu, Chang Ning Li
openaire   +1 more source

Machine performance assessment using Gaussian mixture model (GMM)

2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics, 2008
In this paper, we present a simple and efficient machine performance assessment approach based on Gaussian mixture model (GMM). By only utilizing the machine performance signatures generated from normal machine operation, a GMM can be trained to model the underlying density distribution of the training data.
null Gang Yu, Jun Sun, null Changning Li
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Audiovisual articulatory inversion based on Gaussian Mixture Model (GMM)

2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010
In this study, we examined articulatory inversion using audiovisual information based on Gaussian Mixture Model (GMM). In this method the joint distribution of the articulatory movement and audio (and/or visual) data are modelled via a mixture of Gaussians.
I. Yucel Ozbek, Mubeccel Demirekler
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Speaker Verification Using Gaussian Mixture Model (GMM)

2011
This paper applies GMM for SV on Malay speech. The speaker models were trained on maximum likelihood estimated. The system was evaluated with 23 client speakers with 56 imposters. Malay clean speech data was used. 20 training of 3.5s utterances are used. The best performance achieved using 256-Gaussian imposter model and 32-Gaussian client model gave 3.
Shaikh Salleh, Sheikh Hussain, Ting, C. M., Ariff, A. K., Kamarulafizam, I., Suraya, R. A., Hussain, H.   +5 more
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Acoustic fall detection using Gaussian mixture models and GMM supervectors

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. The proposed system models each fall or noise segment by means of a Gaussian mixture model (GMM) supervector, whose Euclidean distance measures the pairwise difference between
null Xiaodan Zhuang, Jing Huang, Gerasimos Potamianos, Mark Hasegawa-Johnson   +3 more
openaire   +1 more source

Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking

2019 International Conference on Unmanned Aircraft Systems (ICUAS), 2019
In this paper, we have addressed the problem of real-time detection and tracking of dynamic objects using quadrotors. We have developed a novel object detection algorithm by analyzing and matching the color and spacial features of the target from monocular image sequences.
Vishnu Anand, Durgakant Pushp, Rishin Raj, Kaushik Das   +3 more
openaire   +1 more source