Haslett, J.[John],
Maximum likelihood discriminant analysis on the plane using a Markovian
model of spatial context,
PR(18), No. 3-4, 1985, pp. 287-296.
Elsevier DOI
0309
BibRef
Earlier:
Abstract of future paper:
PR(17), No. 6, 1984, pp. Page 677.
Elsevier DOI
0309
BibRef
Venkateswarlu, N.B.,
Raju, P.S.V.S.K.,
Three stage ML classifier,
PR(24), No. 11, 1991, pp. 1113-1116.
Elsevier DOI
0401
fast version of the maximum likelihood classifier.
BibRef
Venkateswarlu, N.B.,
Balaji, S.,
Raju, P.S.V.S.K.,
Boyle, R.D.,
Some further results of three stage ML classification applied to
remotely sensed images,
PR(27), No. 10, October 1994, pp. 1379-1396.
Elsevier DOI
0401
BibRef
Brillault-O'Mahony, B.,
Ellis, T.J.,
A Maximum Likelihood Approach to Feature Segmentation,
PR(26), No. 5, May 1993, pp. 787-798.
Elsevier DOI
BibRef
9305
Zhang, J.,
Modestino, J.W.,
Langan, D.A.,
Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic
Model-Based Image Segmentation,
IP(3), No. 4, July 1994, pp. 404-420.
IEEE DOI
See also Cluster Validation for Unsupervised Stochastic Model-Based Image Segmentation.
BibRef
9407
Fessler, J.A.,
Hero, III, A.O.,
Penalized maximum-likelihood image reconstruction using
space-alternating generalized EM algorithms,
IP(4), No. 10, October 1995, pp. 1417-1429.
IEEE DOI
0402
BibRef
Li, T.F.[Tze Fen],
An efficient algorithm to find the MLE of prior probabilities of a
mixture in pattern recognition,
PR(29), No. 2, February 1996, pp. 337-339.
Elsevier DOI
0401
maximum likelihood estimation.
BibRef
Chen, C.H.,
Tu, T.M.,
Computation Reduction of the Maximum-Likelihood Classifier Using
the Winograd Identity,
PR(29), No. 7, July 1996, pp. 1213-1220.
Elsevier DOI
9607
BibRef
McLachlan, G.J.,
Peel, D.,
Whiten, W.J.,
Maximum likelihood clustering via normal mixture models,
SP:IC(8), No. 2, March 1996, pp. 105-111.
Elsevier DOI
See also Bias associated with the discriminant analysis approach to the estimation of mixing proportions.
BibRef
9603
McLachlan, G.J.[Geoff J.],
Peel, D.,
Mixfit: An Algorithm for the Automatic Fitting and Testing
of Normal Mixture Models,
ICPR98(Vol I: 553-557).
IEEE DOI
9808
BibRef
Zhou, Z.Y.,
Leahy, R.M.,
Qi, J.Y.,
Approximate Maximum-Likelihood Hyperparameter Estimation
for Gibbs-Priors,
IP(6), No. 6, June 1997, pp. 844-861.
IEEE DOI
9705
BibRef
Zhou, Z.Y.,
Leahy, R.M.,
Approximate maximum likelihood hyperparameter estimation for Gibbs
priors,
ICIP95(II: 284-287).
IEEE DOI
9510
BibRef
Handley, J.C.,
Dougherty, E.R.,
Maximum-Likelihood-Estimation for the Two-Dimensional Discrete Boolean
Random Set and Function Models Using Multidimensional Linear Samples,
GMIP(59), No. 4, July 1997, pp. 221-231.
9709
BibRef
Handley, J.C.[John C.],
Dougherty, E.R.[Edward R.],
Maximum-likelihood estimation and optimal filtering in the
nondirectional, one-dimensional binomial germ-grain model,
PR(32), No. 9, September 1999, pp. 1529-1541.
Elsevier DOI
BibRef
9909
Lee, C.,
Choi, E.,
Bayes Error Evaluation of the Gaussian ML Classifier,
GeoRS(38), No. 3, May 2000, pp. 1471-1475.
IEEE Top Reference.
0006
BibRef
Raudys, A.[Aistis],
Long, J.A.,
MLP Based Linear Feature Extraction for Nonlinearly Separable Data,
PAA(4), No. 4 2001, pp. 227-234.
Springer DOI
0202
BibRef
Raudys, A.[Aistis],
Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting
Task,
CIAP05(761-769).
Springer DOI
0509
BibRef
Hayat, M.M.,
Abdullah, M.S.,
Joobeur, A.,
Saleh, B.E.A.,
Maximum-likelihood image estimation using photon-correlated beams,
IP(11), No. 8, August 2002, pp. 838-846.
IEEE DOI
0209
BibRef
Hung, M.C.[Ming-Chih],
Ridd, M.K.[Merrill K.],
A Subpixel Classifier for Urban Land-Cover Mapping Based on a
Maximum-Likelihood Approach and Expert-System Rules,
PhEngRS(68), No. 11, November 2002, pp. 1173-1180.
A supervised classifier based on a maximum-likelihood approach, TM image characteristics, the V-I-S
model, and expert system rules, to estimate ground component composition of urban areas at the subpixel level.
WWW Link.
0304
BibRef
Xie, J.,
Tsui, H.T.,
Image segmentation based on maximum-likelihood estimation and optimum
entropy-distribution (MLE-OED),
PRL(25), No. 10, 16 July 2004, pp. 1133-1141.
Elsevier DOI
0407
BibRef
Xie, J.[Jun],
Tsui, H.T.,
Xia, D.S.[De-Shen],
Multiple objects segmentation based on maximum-likelihood estimation
and optimum entropy-distribution (MLE-OED),
ICPR02(I: 707-710).
IEEE DOI
0211
BibRef
Meignen, S.,
Meignen, H.,
On the Modeling of Small Sample Distributions With Generalized Gaussian
Density in a Maximum Likelihood Framework,
IP(15), No. 6, June 2006, pp. 1647-1652.
IEEE DOI
0606
Model distributions.
BibRef
Pi, M.H.[Ming-Hong],
Improve maximum likelihood estimation for subband GGD parameters,
PRL(27), No. 14, 15 October 2006, pp. 1710-1713.
Elsevier DOI
0609
Generalized Gaussian density; Moment estimator;
Maximum likelihood estimator; Newton-Raphson iteration; Regula-Falsi iteration
BibRef
Zeng, G.L.[Gengsheng L.],
Filtered backprojection algorithm can outperform iterative maximum
likelihood expectation-maximization algorithm,
IJIST(22), No. 2, June 2012, pp. 114-120.
DOI Link
1202
BibRef
Routtenberg, T.,
Tong, L.[Lang],
Joint Frequency and Phasor Estimation Under the KCL Constraint,
SPLetters(20), No. 6, 2013, pp. 575-578.
IEEE DOI
1307
least squares approximations; maximum likelihood estimation;
BibRef
Guo, Q.[Qintian],
Beaulieu, N.C.,
An Approximate ML Estimator for the Location Parameter
of the Generalized Gaussian Distribution With p=5,
SPLetters(20), No. 7, 2013, pp. 677-680.
IEEE DOI
1307
maximum likelihood estimation
BibRef
Zhang, H.,
Wei, P.,
Mou, Q.,
A Semidefinite Relaxation Approach to Blind Despreading
of Long-Code DS-SS Signal With Carrier Frequency Offset,
SPLetters(20), No. 7, 2013, pp. 705-708.
IEEE DOI
1307
Convex functions; maximum likelihood estimate (MLE);
semidefinite relaxation
BibRef
Fang, W.H.,
Lee, Y.C.,
Chen, Y.T.,
Importance Sampling-Based Maximum Likelihood Estimation for
Multidimensional Harmonic Retrieval,
SPLetters(23), No. 1, January 2016, pp. 35-39.
IEEE DOI
1601
Harmonic analysis
BibRef
Babu, P.,
MELT: Maximum-Likelihood Estimation of Low-Rank Toeplitz Covariance
Matrix,
SPLetters(23), No. 11, November 2016, pp. 1587-1591.
IEEE DOI
1609
Toeplitz matrices
BibRef
Strelow, D.[Dennis],
Wang, Q.F.[Qi-Fan],
Si, L.[Luo],
Eriksson, A.P.[Anders P.],
General, Nested, and Constrained Wiberg Minimization,
PAMI(38), No. 9, September 2016, pp. 1803-1815.
IEEE DOI
1609
Algorithm design and analysis
BibRef
Strelow, D.[Dennis],
General and Nested Wiberg Minimization: L2 and Maximum Likelihood,
ECCV12(VII: 195-207).
Springer DOI
1210
BibRef
And:
General and nested Wiberg minimization,
CVPR12(1584-1591).
IEEE DOI
1208
BibRef
Selva, J.,
ML Estimation and Detection of Multiple Frequencies Through
Periodogram Estimate Refinement,
SPLetters(24), No. 3, March 2017, pp. 249-253.
IEEE DOI
1702
Complexity theory
BibRef
Lu, Q.[Qin],
Bar-Shalom, Y.[Yaakov],
Willett, P.[Peter],
Zhou, S.L.[Sheng-Li],
Nonlinear Observation Models With Additive Gaussian Noises and
Efficient MLEs,
SPLetters(24), No. 5, May 2017, pp. 545-549.
IEEE DOI
1704
Gaussian noise
BibRef
Agarwal, R.,
Chen, Z.,
Sarma, S.V.,
A Novel Nonparametric Maximum Likelihood Estimator for Probability
Density Functions,
PAMI(39), No. 7, July 2017, pp. 1294-1308.
IEEE DOI
1706
Computational modeling, Convergence, Kernel,
Maximum likelihood estimation, Probability density function,
Random variables, Maximum likelihood, density, estimation,
neuronal receptive fields, nonparametric, pdf, tail, estimation
BibRef
Ostrometzky, J.,
Messer, H.,
Comparison of Different Methodologies of Parameter-Estimation From
Extreme Values,
SPLetters(24), No. 9, September 2017, pp. 1293-1297.
IEEE DOI
1708
exponential distribution,
simulation, exponential distribution, extreme value theory,
maximum likelihood estimation, parameter estimation, simulation,
Complexity theory, Convergence, Gaussian distribution,
Probability density function, Extreme value theory,
BibRef
Li, P.H.[Pei-Hua],
Wang, Q.L.[Qi-Long],
Zeng, H.,
Zhang, L.,
Local Log-Euclidean Multivariate Gaussian Descriptor and Its
Application to Image Classification,
PAMI(39), No. 4, April 2017, pp. 803-817.
IEEE DOI
1703
BibRef
Earlier: A1, A2, Only:
Local Log-Euclidean Covariance Matrix (L2ECM) for Image Representation
and Its Applications,
ECCV12(III: 469-482).
Springer DOI
1210
Covariance matrices.
either sparse interest points or dense image representations.
BibRef
Wang, Q.L.[Qi-Long],
Li, P.H.[Pei-Hua],
Zhang, L.[Lei],
G2DeNet: Global Gaussian Distribution Embedding Network and Its
Application to Visual Recognition,
CVPR17(6507-6516)
IEEE DOI
1711
Backpropagation, Covariance matrices, Gaussian distribution,
Image representation, Manifolds, Matrix decomposition, Symmetric, matrices
BibRef
Wang, Q.L.[Qi-Long],
Li, P.H.[Pei-Hua],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
RAID-G: Robust Estimation of Approximate Infinite Dimensional
Gaussian with Application to Material Recognition,
CVPR16(4433-4441)
IEEE DOI
1612
BibRef
Zozor, S.,
Ren, C.,
Renaux, A.,
On the Maximum Likelihood Estimator Statistics for Unimodal
Elliptical Distributions in the High Signal-to-Noise Ratio Regime,
SPLetters(25), No. 6, June 2018, pp. 883-887.
IEEE DOI
1806
AWGN, Gaussian noise, maximum likelihood estimation,
signal processing, statistical distributions,
maximum likelihood estimator (MLE) statistics
BibRef
Feitosa, A.E.,
Nascimento, V.H.,
Lopes, C.G.,
Adaptive Detection in Distributed Networks Using Maximum Likelihood
Detector,
SPLetters(25), No. 7, July 2018, pp. 974-978.
IEEE DOI
1807
distributed sensors, least mean squares methods,
maximum likelihood detection, maximum likelihood estimation,
maximum likelihood (ML) detector
BibRef
Ince, E.A.,
Allahdad, M.K.,
Yu, R.,
A Tensor Approach to Model Order Selection of Multiple Sinusoids,
SPLetters(25), No. 7, July 2018, pp. 1104-1108.
IEEE DOI
1807
AWGN, covariance matrices, entropy, maximum likelihood estimation,
signal classification, singular value decomposition, tensors,
order estimation
BibRef
Tronarp, F.,
Karvonen, T.,
Särkkä, S.,
Student's t-Filters for Noise Scale Estimation,
SPLetters(26), No. 2, February 2019, pp. 352-356.
IEEE DOI
1902
covariance matrices, filtering theory, Gaussian processes,
iterative methods, maximum likelihood estimation, optimisation,
noise covariance estimation
BibRef
Psutka, J.V.[Josef V.],
Psutka, J.[Josef],
Sample size for maximum-likelihood estimates of Gaussian model
depending on dimensionality of pattern space,
PR(91), 2019, pp. 25-33.
Elsevier DOI
1904
Maximum-likelihood estimate, Likelihood function,
Gaussian model, Gaussian mixture model, Sample size,
Heteroscedastic data.
BibRef
Mao, L.,
Gao, Y.,
Yan, S.,
Xu, L.,
Persymmetric Subspace Detection in Structured Interference and
Non-Homogeneous Disturbance,
SPLetters(26), No. 6, June 2019, pp. 928-932.
IEEE DOI
1906
Detectors, Interference, Covariance matrices, Adaptation models,
Maximum likelihood estimation, Object detection, Training data,
partially homogeneous environment
BibRef
Le Blanc, J.W.[Joel W.],
Thelen, B.J.[Brian J.],
Hero, A.O.[Alfred O.],
Testing that a Local Optimum of the Likelihood is Globally Optimum
Using Reparameterized Embeddings,
JMIV(62), No. 6-7, July 2020, pp. 858-871.
Springer DOI
2007
BibRef
Huynh, H.T.[Hieu Trung],
Nguyen, L.[Linh],
Nonparametric maximum likelihood estimation using neural networks,
PRL(138), 2020, pp. 580-586.
Elsevier DOI
2010
Neural network, Maximum likelihood estimation, Nonparametric,
Probability density function
BibRef
Vidal, A.F.[Ana Fernandez],
de Bortoli, V.[Valentin],
Pereyra, M.[Marcelo],
Durmus, A.[Alain],
Maximum Likelihood Estimation of Regularization Parameters in
High-Dimensional Inverse Problems: An Empirical Bayesian Approach
Part I: Methodology and Experiments,
SIIMS(13), No. 4, 2020, pp. 1945-1989.
DOI Link
2012
BibRef
de Bortoli, V.[Valentin],
Durmus, A.[Alain],
Pereyra, M.[Marcelo],
Vidal, A.F.[Ana Fernandez],
Maximum Likelihood Estimation of Regularization Parameters in
High-Dimensional Inverse Problems: An Empirical Bayesian Approach.
Part II: Theoretical Analysis,
SIIMS(13), No. 4, 2020, pp. 1990-2028.
DOI Link
2012
BibRef
Manss, C.,
Shutin, D.,
Leus, G.,
Consensus Based Distributed Sparse Bayesian Learning by Fast Marginal
Likelihood Maximization,
SPLetters(27), 2020, pp. 2119-2123.
IEEE DOI
2012
Signal processing algorithms, Optimization, Bayes methods,
Estimation, Robot sensing systems, Convergence, Convex functions,
sparse bayesian learning
BibRef
Naumer, H.,
Kamalabadi, F.,
Estimation of Linear Space-Invariant Dynamics,
SPLetters(27), 2020, pp. 2154-2158.
IEEE DOI
2012
Maximum likelihood estimation, Mathematical model,
Heuristic algorithms, Steady-state, Large scale integration,
dynamical systems
BibRef
Efendi, E.,
Dulek, B.,
Online EM-Based Ensemble Classification With Correlated Agents,
SPLetters(28), 2021, pp. 294-298.
IEEE DOI
2102
Signal processing algorithms, Parameter estimation,
Maximum likelihood estimation, Correlation, Indexes,
expectation-maximization
BibRef
Lesouple, J.,
Pilastre, B.,
Altmann, Y.,
Tourneret, J.Y.,
Hypersphere Fitting From Noisy Data Using an EM Algorithm,
SPLetters(28), 2021, pp. 314-318.
IEEE DOI
2102
Signal processing algorithms, Maximum likelihood estimation,
Noise measurement, Fitting,
von Mises-Fisher distribution
BibRef
Li, Y.P.[Yun-Peng],
Ye, Z.H.[Zhao-Hui],
Boosting in Univariate Nonparametric Maximum Likelihood Estimation,
SPLetters(28), 2021, pp. 623-627.
IEEE DOI
2104
Boosting, Kernel, Splines (mathematics), Smoothing methods,
Mathematical model, Signal processing algorithms,
smoothing spline
BibRef
Zhao, Y.[Yan],
Wong, W.[Wai],
Zheng, J.F.[Jian-Feng],
Liu, H.X.[Henry X.],
Maximum Likelihood Estimation of Probe Vehicle Penetration Rates and
Queue Length Distributions From Probe Vehicle Data,
ITS(23), No. 7, July 2022, pp. 7628-7636.
IEEE DOI
2207
Probes, Maximum likelihood estimation, Detectors,
Queueing analysis, Trajectory, Real-time systems, Transportation,
maximum likelihood estimation
BibRef
Llosa-Vite, C.[Carlos],
Maitra, R.[Ranjan],
Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis
of Variance,
PAMI(45), No. 2, February 2023, pp. 2282-2296.
IEEE DOI
2301
Tensors, Analysis of variance, Faces,
Maximum likelihood estimation, Linear regression, tucker format
BibRef
Tucker, D.[David],
Zhao, S.[Shen],
Potter, L.C.[Lee C],
Maximum Likelihood Estimation in Mixed Integer Linear Models,
SPLetters(30), 2023, pp. 1557-1561.
IEEE DOI
2311
BibRef
Teimouri, M.[Mahdi],
A Fast and Simple Algorithm for Computing MLE of the Amplitude
Density Function Parameters,
SPLetters(31), 2024, pp. 626-630.
IEEE DOI
2402
Maximum likelihood estimation, Synthetic aperture radar,
Probability density function, GSM, Clutter, Tail,
synthetic aperture radar (SAR)
BibRef
Ye, F.[Fei],
Bors, A.G.[Adrian G.],
InfoVAEGAN: Learning Joint Interpretable Representations by
Information Maximization and Maximum Likelihood,
ICIP21(749-753)
IEEE DOI
2201
Training, Manifolds, Inference mechanisms, Tools,
Generative adversarial networks, Generators, Mutual information
BibRef
Ali, M.[Muhammad],
Gao, J.B.[Jun-Bin],
Antolovich, M.[Michael],
MLE-Based Learning on Grassmann Manifolds,
DICTA16(1-7)
IEEE DOI
1701
Computer vision
BibRef
Harba, R.[Rachid],
Douzi, H.[Hassan],
El Hajji, M.[Mohamed],
Maximum Likelihood Estimation, Interpolation and Prediction for
Fractional Brownian Motion,
ICISP12(326-332).
Springer DOI
1208
BibRef
Pletscher, P.[Patrick],
Nowozin, S.[Sebastian],
Kohli, P.[Pushmeet],
Rother, C.[Carsten],
Putting MAP Back on the Map,
DAGM11(111-121).
Springer DOI
1109
Learning Conditional Random Fields (CRFs) models.
BibRef
Okatani, T.[Takayuki],
Deguchi, K.[Koichiro],
Improving accuracy of geometric parameter estimation using projected
score method,
ICCV09(1733-1740).
IEEE DOI
0909
BibRef
And:
On bias correction for geometric parameter estimation in computer
vision,
CVPR09(959-966).
IEEE DOI
0906
Bias in maximum likelihood estimation techniques due to geometric
configurations.
BibRef
Rastgar, H.[Houman],
Zhang, L.[Liang],
Wang, D.[Demin],
Dubois, E.[Eric],
Validation of correspondences in MLESAC robust estimation,
ICPR08(1-4).
IEEE DOI
0812
maximum likelihood estimation sample consensus.
BibRef
Nestares, O.,
Fleet, D.J.,
Error-in-variables likelihood functions for motion estimation,
ICIP03(III: 77-80).
IEEE DOI
0312
BibRef
Nestares, O.[Oscar],
Fleet, D.J.[David J.],
Heeger, D.J.[David J.],
Likelihood Functions and Confidence Bounds for Total-Least-Squares
Problems,
CVPR00(I: 523-530).
IEEE DOI
0005
BibRef
Um, I.T.,
Ra, J.H.,
Kim, M.H.,
Comparison of Clustering Methods for MLP-based Speaker Verification,
ICPR00(Vol II: 475-478).
IEEE DOI
0009
BibRef
El Malek, J.,
Alimi, A.M.,
Tourki, R.,
Effect of the Feature Vector Size on the Generalization Error:
The Case of MLPNN and RBFNN Classifiers,
ICPR00(Vol II: 630-633).
IEEE DOI
0009
BibRef
Gimel'farb, G.L.[Georgy L.],
On the Maximum Likelihood Potential Estimates
for Gibbs Random Field Image Models,
ICPR98(Vol II: 1598-1600).
IEEE DOI
9808
BibRef
Grim, J.,
Maximum-Likelihood Design of Layered Neural Networks,
ICPR96(IV: 85-89).
IEEE DOI
9608
(Academy of Sciences, CZ)
BibRef
Berrim, S.,
Lansiart, A.,
Moretti, J.L.,
Implementing of maximum likelihood in tomographical coded aperture,
ICIP96(II: 745-748).
IEEE DOI
9610
BibRef
Sun, Y.[Yi],
Tracking and detection of moving point targets in noise image sequences
by local maximum likelihood,
ICIP96(III: 799-802).
IEEE DOI
9610
BibRef
Moghaddam, B.,
Pentland, A.,
A subspace method for maximum likelihood target detection,
ICIP95(III: 512-515).
IEEE DOI
9510
BibRef
Meir, R.,
Empirical risk minimization versus maximum-likelihood estimation: A
case study,
ICPR94(B:295-299).
IEEE DOI
9410
BibRef
Endoh, T.,
Toriu, T.,
Tagawa, N.,
The maximum likelihood estimator is not 'optimal' on 3-D motion
estimation from noisy optical flow,
ICIP94(II: 247-251).
IEEE DOI
9411
BibRef
Tagawa, N.,
Toriu, T.,
Endoh, T.,
An objective function for 3-D motion estimation from optical flow with
lower error variance than maximum likelihood estimator,
ICIP94(II: 252-256).
IEEE DOI
9411
BibRef
Schultz, R.R.,
Stevenson, R.L.,
Lumsdaine, A.,
Maximum likelihood parameter estimation for non-Gaussian prior signal
models,
ICIP94(II: 700-704).
IEEE DOI
9411
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Energy Minimization, Energy Maximization Computation, Function Solving .