Results 211 to 220 of about 64,560 (254)
Advanced Robotics Research, EarlyView.Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
Advanced Robotics Research, EarlyView.This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
A Robust Multivariate Thresholding Function for Sparse and Biomedical Signal Reconstruction. [PDF]
Sensors (Basel)Ullah H, Gaire S, Graves CA.
europepmc +1 more source
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Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Honghai Liu, Zhaojie Ju
exaly +3 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Honghai Liu, Zhaojie Ju
exaly +3 more sources
Hierarchical Gaussian mixture model
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified.
Vincent Garcia +2 more
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Splitting Gaussians in Mixture Models
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker ...
Rubén Heras Evangelio +2 more
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Parsimonious Gaussian mixture models
Statistics and Computing, 2008Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases.
Paul David McNicholas +1 more
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Modelling profiles with a mixture of Gaussians
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be
James Orwell +3 more
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Combining Gaussian Mixture Models
2004A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters ...
Hyoungjoo Lee, Sungzoon Cho
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Detection in underwater noises modeled as a Gaussian-Gaussian mixture
ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005We study statistical modeling by a Gaussian-Gaussian mixture for two different underwater noise samples. We show that one of them can be adequately described by a Gaussian-Gaussian mixture whereas the other one is very close to a Gaussian model and is described by a mixture with a very small perturbating term.
Michel Bouvet, Stuart C. Schwartz
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