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» A Privacy Preserving Framework for Gaussian Mixture Models
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ICCV
2011
IEEE
12 years 10 months ago
A Nonparametric Riemannian Framework on Tensor Field with Application to Foreground Segmentation
Background modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent resear...
Rui Caseiro, João F. Henriques, Pedro Martins, Jo...
TVCG
2012
191views Hardware» more  TVCG 2012»
12 years 8 days ago
Live Speech Driven Head-and-Eye Motion Generators
—This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its cent...
Binh Huy Le, Xiaohan Ma, Zhigang Deng
CEC
2008
IEEE
14 years 4 months ago
NichingEDA: Utilizing the diversity inside a population of EDAs for continuous optimization
— Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian mod...
Weishan Dong, Xin Yao
TIP
2008
205views more  TIP 2008»
13 years 9 months ago
Image Denoising Using Derotated Complex Wavelet Coefficients
A method for removing additive Gaussian noise from digital images is described. It is based on statistical modeling of the coefficients of a redundant, oriented, complex multiscale...
Mark Miller, Nick G. Kingsbury
INTERSPEECH
2010
13 years 4 months ago
Canonical state models for automatic speech recognition
Current speech recognition systems are often based on HMMs with state-clustered Gaussian Mixture Models (GMMs) to represent the context dependent output distributions. Though high...
Mark J. F. Gales, Kai Yu