Results 31 to 40 of about 33,807 (240)
POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions.
S. Rouabah, M. Ouarzeddine, B. Azmedroub
doaj +1 more source
CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement
IEEE Access, 2021 Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method on top of one recent architecture with a novel explanation of Gaussian mixture model (GMM) membership ...
Chunzhi Gu +3 more
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Incrementally Learned Mixture Models for GNSS Localization
, 2020 GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the ...
Pfeifer, Tim, Protzel, Peter
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Jixie qiangdu, 2023 In order to study the frequency domain calculation method of non-Gaussian excitation, Gaussian mixture model（GMM） is introduced. The measured non-Gaussian excitation is transformed into probabilistic power spectrum（PPSD） through GMM model, so that the ...
XU Yang +3 more
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Global soil moisture bimodality in satellite observations and climate models [PDF]
, 2017 A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models.
de Jeu, RAM +3 more
core +2 more sources
Recognition of Aircraft Engine Sound Based on GMM-UBM Model
MATEC Web of Conferences, 2017 Gaussian mixture model-universal background model (GMM-UBM) is a commonly-used speaker recognition technology, and which has achieved good effect for detection speaker’s sound.
Yuan Shuai, Sun Chengli, Yang Haoge
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Effective Learning of a GMRF Mixture Model
IEEE Access, 2022 Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method ...
Shahaf E. Finder +2 more
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Compositional Model based Fisher Vector Coding for Image Classification
, 2017 Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM)
Hengel, Anton van den +6 more
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Neuroscience Informatics, 2021 Among statistical models, Gaussian Mixture Models (GMMs) have been used in numerous applications to model the data in which a mixture of Gaussian curves fits them. Several methods have been introduced to estimate the optimum parameters to a GMM fitted to
Mehran Azimbagirad +1 more
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Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source