Edge guided single depth image super resolution
Jun Xie, Rogerio Schmidt Feris, et al.
ICIP 2014
This paper proposes a new covariance modeling technique for Gaussian Mixture Models. Specifically the inverse covariance (precision) matrix of each Gaussian is expanded in a rank-1 basis i.e., ∑j-1 = Pj = ∑k=1D λkjakakT, λkj ∈ ℝ, ak ∈ ℝd. A generalized EM algorithm is proposed to obtain maximum likelihood parameter estimates for the basis set {akakT} k=1D and the expansion coefficients {λ kj}. This model, called the Extended Maximum Likelihood Linear Transform (EMLLT) model, is extremely flexible: by varying the number of basis elements from D = d to D = d(d + 1)/2 one gradually moves from a Maximum Likelihood Linear Transform (MLLT) model to a full-covariance model. Experimental results on two speech recognition tasks show that the EMLLT model can give relative gains of up to 35% in the word error rate over a standard diagonal covariance model, 30% over a standard MLLT model.
Jun Xie, Rogerio Schmidt Feris, et al.
ICIP 2014
Mahesh Viswanathan, Homayoon S.M. Beigi, et al.
ICDAR 1999
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021