Binford, T.O.,
Levitt, T.S., and
Mann, W.B.,
Bayesian Inference in Model-Based Machine Vision,
Uncertainty in AI(3), 1989, pp. XX-YY.
Levitt, Kanal and Lemmer (Eds.),
BibRef
8900
North HollandPreliminary version of the previous paper.
BibRef
Binford, T.O.[Thomas O.], and
Mann, W.B.[Wallace B.],
Probabilities for Bayesian Networks in Vision,
ARPA94(I:633-643).
BibRef
9400
Mann, W.B., and
Binford, T.O.,
An Example of 3-D Interpretation of Images Using Bayesian Networks,
DARPA92(793-801).
Generalized Cylinder.
Matching, 3-D.
BibRef
9200
Levitt, T.S.,
Binford, T.O.,
Ettinger, G.J., and
Gelband, P.,
Probability-Based Control for Computer Vision,
DARPA89(355-369).
Probability. Implementation of recognition using Bayesian networks.
BibRef
8900
Binford, T.O.,
Levitt, T.S.,
Evidential reasoning for object recognition,
PAMI(25), No. 7, July 2003, pp. 837-851.
IEEE Abstract.
0307
Key to vision is to match generic models to the scene.
Object centered models with generic model classes.
BibRef
Goldszmidt, M.,
Morris, P.,
Pearl, J.,
A maximum entropy approach to nonmonotonic reasoning,
PAMI(15), No. 3, March 1993, pp. 220-232.
IEEE DOI
0401
BibRef
Dagum, P.,
Chavez, R.M.,
Approximating probabilistic inference in Bayesian belief networks,
PAMI(15), No. 3, March 1993, pp. 246-255.
IEEE DOI
0401
BibRef
Olesen, K.G.,
Causal probabilistic networks with both discrete and continuous
variables,
PAMI(15), No. 3, March 1993, pp. 275-279.
IEEE DOI
0401
BibRef
Gregor, J.,
Thomason, M.G.,
Hybrid pattern recognition using Markov networks,
PAMI(15), No. 6, June 1993, pp. 651-656.
IEEE DOI
0401
BibRef
Kumar, V.P.,
Desai, U.B.,
Image Interpretation Using Bayesian Networks,
PAMI(18), No. 1, January 1996, pp. 74-77.
IEEE DOI
Bayes Nets.
See also Joint segmentation and image interpretation.
BibRef
9601
Larranaga, P.,
Poza, M.,
Yurramendi, Y.,
Murga, R.H.,
Kuijpers, C.M.H.,
Structure Learning of Bayesian Networks By Genetic Algorithms:
A Performance Analysis of Control Parameters,
PAMI(18), No. 9, September 1996, pp. 912-926.
IEEE DOI
Bayes Nets.
Genetic Algorithms.
Performance Analysis.
BibRef
9609
Tsai, W.H., and
Fu, K.S.,
A Pattern Deformation Model and Bayes Error-Correcting
Recognition System,
SMC(9), No. 12, December 1979, pp. 745-756.
Bayes Nets.
BibRef
7912
Cowell, R.G.,
On Compatible Priors for Bayesian Networks,
PAMI(18), No. 9, September 1996, pp. 901-911.
IEEE DOI
Bayes Nets.
Graph Structures.
BibRef
9609
Carstensen, J.M.,
An Active Lattice Model in a Bayesian Framework,
CVIU(63), No. 2, March 1996, pp. 380-387.
Bayes Nets.
DOI Link
BibRef
9603
Szeliski, R.S.[Richard Stephen],
Bayesian Modeling of Uncertainty in Low-Level Vision,
IJCV(5), No. 3, December 1990, pp. 271-302.
Springer DOI
BibRef
9012
And:
Hingham MA:
KluwerAcademic, September 1989, ISBN 0-7923-9039-3
WWW Link.
BibRef
And:
CMU-CS-TR-88-169, August 1988.
BibRef
Ph.D.Thesis (CS), 1989.
Uncertainty.
BibRef
Corridoni, J.M.,
del Bimbo, A.,
Landi, L.,
3D Object Classification Using Multiobject Kohonen Networks,
PR(29), No. 6, June 1996, pp. 919-935.
Elsevier DOI
9606
BibRef
Fairwood, R.C.,
Barreau, G.,
A Belief Network for the Recognition of 3D Geometric Primitives,
CIAP91(414-421).
BibRef
9100
Abdel-Mottaleb, M.[Mohamed],
Rosenfeld, A.[Azriel],
Inexact Bayesian Estimation,
PR(25), No. 6, June 1992, pp. 641-646.
Elsevier DOI
BibRef
9206
Abdel-Mottaleb, M.[Mohamed],
Rosenfeld, A.[Azriel],
Qualitative Bayesian Estimation Of Digital Signals And Images,
PR(25), No. 11, November 1992, pp. 1371-1380.
Elsevier DOI
BibRef
9211
Liau, C.J.[Churn-Jung], and
Lin, B.I.P.[Bertrand I-Peng],
Possibilistic Reasoning: A Mini-Survey and Uniform Semantics,
AI(88), No. 1-2, December 1996, pp. 163-193.
Elsevier DOI
9701
BibRef
Kwoh, C.K.[Chee-Keong], and
Gillies, D.F.[Duncan Fyfe],
Using Hidden Nodes in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 1-38.
Elsevier DOI
9701
BibRef
Poole, D.[David],
Probabilistic Conflicts in a Search Algorithm for
Estimating Posterior Probabilities in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 69-100.
Elsevier DOI
9701
BibRef
Kleiter, G.D.[Gernot D.],
Propagating Imprecise Probabilities in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 143-161.
Elsevier DOI
9701
BibRef
Zhang, N.L.W.[Nevin Lian-Wen],
Irrelevance and Parameter Learning in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 359-373.
Elsevier DOI
9701
BibRef
van Engelen, R.A.,
Approximating Bayesian Belief Networks By Arc Removal,
PAMI(19), No. 8, August 1997, pp. 916-920.
IEEE DOI
9709
BibRef
Pynadath, D.V.,
Wellman, M.P.,
Generalized Queries on Probabilistic Context-Free Grammars,
PAMI(20), No. 1, January 1998, pp. 65-77.
IEEE DOI
9803
BibRef
Lam, W.,
Bayesian Network Refinement Via Machine Learning Approach,
PAMI(20), No. 3, March 1998, pp. 240-251.
IEEE DOI
9805
General Bayesian network paper.
BibRef
Nagy, G.,
Xu, Y.H.,
Bayesian Subsequence Matching and Segmentation,
PRL(18), No. 11-13, November 1997, pp. 1117-1124.
9806
BibRef
Williams, M.L.[Mark L.],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Multiple Graph Matching with Bayesian-Inference,
PRL(18), No. 11-13, November 1997, pp. 1275-1281.
9806
BibRef
Earlier:
Multi-sensor fusion with Bayesian inference,
CAIP97(25-32).
Springer DOI
9709
BibRef
Calder, B.R.,
Linnett, L.M.,
Carmichael, D.R.,
Bayesian Approach to Object Detection in Sidescan Sonar,
VISP(145), No. 3, June 1998, pp. 221-228.
9808
BibRef
Earlier:
Constrained Image Restoration with a Multinomial Prior,
ICIP97(I: 259-262).
IEEE DOI
BibRef
Roberts, S.J.,
Independent Component Analysis:
Source Assessment and Separation, a Bayesian Approach,
VISP(145), No. 3, June 1998, pp. 149-154.
9808
BibRef
Foresti, G.L.[Gian Luca],
Pieroni, G.[Goffredo],
Exploiting Neural Trees in Range Image Understanding,
PRL(19), No. 9, 31 July 1998, pp. 869-878.
BibRef
9807
Wong, M.L.[Man Leung],
Lam, W.[Wai],
Leung, K.S.[Kwong Sak],
Using Evolutionary Programming and Minimum Description Length Principle
for Data Mining of Bayesian Networks,
PAMI(21), No. 2, February 1999, pp. 174-178.
IEEE DOI
BibRef
9902
Peña, J.M.,
Lozano, J.A.,
Larrañaga, P.,
Learning Bayesian networks for clustering by means of constructive
induction,
PRL(20), No. 11-13, November 1999, pp. 1219-1230.
0001
BibRef
Peña, J.M.,
Lozano, J.A.,
Larrañaga, P.,
An improved Bayesian structural EM algorithm for learning Bayesian
networks for clustering,
PRL(21), No. 6-7, June 2000, pp. 779-786.
0006
BibRef
Santafe, G.,
Lozano, J.A.,
Larranaga, P.,
Bayesian Model Averaging of Naive Bayes for Clustering,
SMC-B(36), No. 5, October 2006, pp. 1149-1161.
IEEE DOI
0609
BibRef
Calvo, B.[Borja],
Larranaga, P.[Pedro],
Lozano, J.A.[Jose A.],
Learning Bayesian classifiers from positive and unlabeled examples,
PRL(28), No. 16, December 2007, pp. 2375-2384.
Elsevier DOI
0711
Positive unlabeled learning; Bayesian classifiers; Naive Bayes;
Tree augmented naive Bayes; Bayesian approach
BibRef
Hernández-González, J.[Jerónimo],
Inza, I.[Iñaki],
Lozano, J.A.[Jose A.],
Learning Bayesian network classifiers from label proportions,
PR(46), No. 12, 2013, pp. 3425-3440.
Elsevier DOI
1307
Supervised classification
BibRef
Calvo, B.[Borja],
Larranaga, P.[Pedro],
Lozano, J.A.[Jose A.],
Feature subset selection from positive and unlabelled examples,
PRL(30), No. 11, 1 August 2009, pp. 1027-1036.
Elsevier DOI
0909
Positive unlabelled learning; Partially supervised classification;
Feature subset selection; Filter methods
BibRef
Rodriguez, J.D.[Juan D.],
Perez, A.[Aritz],
Lozano, J.A.[Jose A.],
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error
Estimation,
PAMI(32), No. 3, March 2010, pp. 569-575.
IEEE DOI
1002
Performance of learning more difficult in real world where errors may not
be know. Estimate the performance.
BibRef
Peña, J.M.[Jose M.],
Björkegren, J.[Johan],
Tegnér, J.[Jesper],
Learning dynamic Bayesian network models via cross-validation,
PRL(26), No. 14, 15 October 2005, pp. 2295-2308.
Elsevier DOI
0510
BibRef
Larsen, R.[Rasmus],
3-D Contextual Bayesian Classifiers,
IP(9), No. 3, March 2000, pp. 518-524.
IEEE DOI
0003
BibRef
Earlier:
A 3-D Contextual Bayesian Classifier,
SCIA97(xx-yy)
HTML Version.
9705
BibRef
Oswald, N.[Norbert],
Levi, P.[Paul],
Cooperative object recognition,
PRL(22), No. 12, October 2001, pp. 1273-1282.
Elsevier DOI
0108
BibRef
Earlier:
Cooperative vision in a multi-agent architecture,
Springer DOI
9709
Integrate multiple hypotheses from different observers.
BibRef
Olesen, K.G.,
Madsen, A.L.,
Maximal Prime Subgraph Decomposition of Bayesian Networks,
SMC-B(32), No. 1, February 2002, pp. 21-31.
IEEE Top Reference.
0202
BibRef
Marengoni, M.[Mauricio],
Hanson, A.R.[Allen R.],
Zilberstein, S.[Shlomo],
Riseman, E.M.,
Decision making and uncertainty management in a 3D reconstruction
system,
PAMI(25), No. 7, July 2003, pp. 852-858.
IEEE Abstract.
0307
BibRef
Earlier:
And:
Cost and Information-Driven Algorithm Selection for Vision Systems,
ICIAR04(I: 519-529).
Springer DOI
0409
BibRef
Control in a 3D Reconstruction System using Selective Perception,
ICCV99(1229-1236).
IEEE DOI High level control of vision algorithms using
Bayesian networks.
BibRef
Bowden, R.[Richard],
Special Issue Introduction, Bayesian Analysis,
IVC(21), No. 9, September 2003, pp. Page 841.
Elsevier DOI
0308
BibRef
Bromiley, P.A.,
Thacker, N.A.,
Scott, M.L.J.,
Pokric, M.,
Lacey, A.J.,
Cootes, T.F.,
Bayesian and non-Bayesian probabilistic models for medical image
analysis,
IVC(21), No. 9, September 2003, pp. 851-864.
Elsevier DOI
0308
BibRef
Cazorla, M.A.,
Escolano, F.,
Two bayesian methods for junction classification,
IP(12), No. 3, March 2003, pp. 317-327.
IEEE DOI
0301
BibRef
Knill, D.C.[David C.],
Friedman, W.T.[William T.],
Geisler, W.S.[Wilson S.],
Bayesian and Statistical Approaches to Vision,
JOSA-A(20), No. 7, July 2003, pp. 1232-1233.
WWW Link.
0307
BibRef
Mohammad-Djafari, A.[Ali],
Bayesian inference for inverse problems in signal and image processing
and applications,
IJIST(16), No. 5, 2006, pp. 209-214.
DOI Link
0704
BibRef
Mohammad-Djafari, A.[Ali],
Féron, O.[Olivier],
Bayesian approach to change points detection in time series,
IJIST(16), No. 5, 2006, pp. 215-221.
DOI Link
0704
BibRef
Leibe, B.[Bastian],
Ettlin, A.[Alan],
Schiele, B.[Bernt],
Learning semantic object parts for object categorization,
IVC(26), No. 1, 1 January 2008, pp. 15-26.
Elsevier DOI
0711
Object recognition; Object categorization; Part-based representations;
Semantic; Bayesian networks
BibRef
Robin, A.,
Le Hégarat-Mascle, S.,
Moisan, L.[Lionel],
Unsupervised Subpixelic Classification Using Coarse-Resolution Time
Series and Structural Information,
GeoRS(46), No. 5, May 2008, pp. 1359-1374.
IEEE DOI
0804
BibRef
Kallel, A.[Abdelaziz],
Le Hégarat-Mascle, S.[Sylvie],
Hubert-Moy, L.,
Ottlé, C.,
Fusion of Vegetation Indices Using Continuous Belief Functions and
Cautious-Adaptive Combination Rule,
GeoRS(46), No. 5, May 2008, pp. 1499-1513.
IEEE DOI
0804
BibRef
Le Hégarat-Mascle, S.[Sylvie],
Kallel, A.[Abdelaziz],
Descombes, X.[Xavier],
Use of Ant Colony Optimization for Finding Neighborhoods in Image
Non-stationary Markov Random Field Classification,
PReMI07(279-286).
Springer DOI
0712
BibRef
Cheng, H.H.[Huan-Huan],
Wang, R.S.[Run-Sheng],
Semantic modeling of natural scenes based on contextual Bayesian
networks,
PR(43), No. 12, December 2010, pp. 4042-4054.
Elsevier DOI
1003
Scene classification; Image representation; Bayesian network; Spatial
information; Semantic features
BibRef
Lu, Z.J.[Zhao-Jin],
Lee, S.[Sukhan],
Probabilistic 3D object recognition and pose estimation using multiple
interpretations generation,
JOSA-A(28), No. 12, December 2011, pp. 2607-2618.
WWW Link.
1112
BibRef
Lee, S.[Sukhan],
Lu, Z.J.[Zhao-Jin],
Kim, H.W.[Hyun-Woo],
Probabilistic 3D object recognition with both positive and negative
evidences,
ICCV11(2360-2367).
IEEE DOI
1201
BibRef
Earlier: A2, A1, A3:
Probabilistic 3D Object Recognition Based on Multiple Interpretations
Generation,
ACCV10(IV: 333-346).
Springer DOI
1011
BibRef
Li, C.Q.[Chao-Qun],
Li, H.W.[Hong-Wei],
A Modified Short and Fukunaga Metric based on the attribute
independence assumption,
PRL(33), No. 9, 1 July 2012, pp. 1213-1218.
Elsevier DOI
1202
Naive Bayes; Attribute independence assumption; Short and Fukunaga
Metric; Value Difference Metric; Distance-related algorithms
BibRef
Battistelli, G.,
Chisci, L.,
Fantacci, C.,
Parallel Consensus on Likelihoods and Priors for Networked Nonlinear
Filtering,
SPLetters(21), No. 7, July 2014, pp. 787-791.
IEEE DOI
1405
Bayes methods
BibRef
Jiang, L.X.[Liang-Xiao],
Li, C.Q.[Chao-Qun],
Wang, S.S.[Sha-Sha],
Cost-sensitive Bayesian network classifiers,
PRL(45), No. 1, 2014, pp. 211-216.
Elsevier DOI
1407
Cost-sensitive learning
BibRef
Schachtner, R.,
Poeppel, G.,
Tomé, A.M.,
Lang, E.W.,
A Bayesian approach to the Lee-Seung update rules for NMF,
PRL(45), No. 1, 2014, pp. 251-256.
Elsevier DOI
1407
Variational Bayes NMF
BibRef
Bielza, C.[Concha],
Larrañaga, P.[Pedro],
Discrete Bayesian Network Classifiers: A Survey,
Surveys(47), No. 1, July 2014, pp. Article No 5.
DOI Link
1408
Survey, Bayes Nets. Survey the whole set of discrete Bayesian network classifiers devised
to date, organized in increasing order of structure complexity: naive
Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian
classifiers, k-dependence Bayesian classifiers, Bayesian
network-augmented naive Bayes, Markov blanket-based Bayesian
classifier, unrestricted Bayesian classifiers, and Bayesian multinets.
Issues of feature subset selection and generative and discriminative
structure and parameter learning are also covered.
BibRef
Katselis, D.,
Rojas, C.R.,
Application-Oriented Estimator Selection,
SPLetters(22), No. 4, April 2015, pp. 489-493.
IEEE DOI
1411
Bayes methods
BibRef
Kailkhura, B.,
Han, Y.S.,
Brahma, S.,
Varshney, P.K.,
Asymptotic Analysis of Distributed Bayesian Detection with Byzantine
Data,
SPLetters(22), No. 5, May 2015, pp. 608-612.
IEEE DOI
1411
Bayes methods
BibRef
Kim, S.,
Valente, F.,
Filippone, M.,
Vinciarelli, A.,
Predicting Continuous Conflict Perception with Bayesian Gaussian
Processes,
AffCom(5), No. 2, April 2014, pp. 187-200.
IEEE DOI
1411
Bayes methods
BibRef
Steinberg, D.M.[Daniel M.],
Pizarro, O.[Oscar],
Williams, S.B.[Stefan B.],
Hierarchical Bayesian models for unsupervised scene understanding,
CVIU(131), No. 1, 2015, pp. 128-144.
Elsevier DOI
1412
Scene understanding
BibRef
de Blasi, P.,
Favaro, S.,
Lijoi, A.,
Mena, R.H.,
Prunster, I.,
Ruggiero, M.,
Are Gibbs-Type Priors the Most Natural Generalization of the
Dirichlet Process?,
PAMI(37), No. 2, February 2015, pp. 212-229.
IEEE DOI
1502
Analytical models
BibRef
Dai, A.M.,
Storkey, A.J.,
The Supervised Hierarchical Dirichlet Process,
PAMI(37), No. 2, February 2015, pp. 243-255.
IEEE DOI
1502
Bayes methods
BibRef
Paisley, J.,
Wang, C.,
Blei, D.M.,
Jordan, M.I.,
Nested Hierarchical Dirichlet Processes,
PAMI(37), No. 2, February 2015, pp. 256-270.
IEEE DOI
1502
Atomic measurements
BibRef
Broderick, T.,
Mackey, L.,
Paisley, J.,
Jordan, M.I.,
Combinatorial Clustering and the Beta Negative Binomial Process,
PAMI(37), No. 2, February 2015, pp. 290-306.
IEEE DOI
1502
Analytical models
BibRef
Jampani, V.[Varun],
Nowozin, S.[Sebastian],
Loper, M.[Matthew],
Gehler, P.V.[Peter V.],
The informed sampler: A discriminative approach to Bayesian inference
in generative computer vision models,
CVIU(136), No. 1, 2015, pp. 32-44.
Elsevier DOI
1506
Probabilistic models
BibRef
Osokin, A.[Anton],
Vetrov, D.[Dmitry],
Submodular Relaxation for Inference in Markov Random Fields,
PAMI(37), No. 7, July 2015, pp. 1347-1359.
IEEE DOI
1506
BibRef
Earlier:
Submodular Relaxation for MRFs with High-Order Potentials,
Global12(III: 305-314).
Springer DOI
1210
Joints
BibRef
Osokin, A.[Anton],
Vetrov, D.[Dmitry],
Kolmogorov, V.[Vladimir],
Submodular decomposition framework for inference in associative Markov
networks with global constraints,
CVPR11(1889-1896).
IEEE DOI
1106
Most probable state of discrete MRF.
Divide and conquer.
BibRef
Bratieres, S.,
Quadrianto, N.,
Ghahramani, Z.,
GPstruct: Bayesian Structured Prediction Using Gaussian Processes,
PAMI(37), No. 7, July 2015, pp. 1514-1520.
IEEE DOI
1506
Bayes methods
BibRef
Huemmer, C.,
Maas, R.,
Kellermann, W.,
The NLMS Algorithm with Time-Variant Optimum Stepsize Derived from a
Bayesian Network Perspective,
SPLetters(22), No. 11, November 2015, pp. 1874-1878.
IEEE DOI
1509
Bayes methods
BibRef
Nurminen, H.,
Ardeshiri, T.,
Piche, R.,
Gustafsson, F.,
Robust Inference for State-Space Models with Skewed Measurement Noise,
SPLetters(22), No. 11, November 2015, pp. 1898-1902.
IEEE DOI
1509
Bayes methods
BibRef
Coluccia, A.,
Regularized Covariance Matrix Estimation via Empirical Bayes,
SPLetters(22), No. 11, November 2015, pp. 2127-2131.
IEEE DOI
1509
Bayes methods
BibRef
Bordin, C.J.,
Bruno, M.G.S.,
Sequential Bayesian Algorithms for Identification and Blind
Equalization of Unit-Norm Channels,
SPLetters(22), No. 11, November 2015, pp. 2157-2161.
IEEE DOI
1509
FIR filters
BibRef
Zou, Y.[Yuan],
Pensar, J.[Johan],
Roos, T.[Teemu],
Representing local structure in Bayesian networks by Boolean
functions,
PRL(95), No. 1, 2017, pp. 73-77.
Elsevier DOI
1708
Bayesian networks
BibRef
Ko, S.[Song],
Lim, H.K.[Hyun-Ki],
Kim, D.W.[Dae-Won],
Reverse engineering for causal discovery based on monotonic
characteristic of causal structure,
PRL(95), No. 1, 2017, pp. 91-97.
Elsevier DOI
1708
Bayesian networks
BibRef
He, C.[Chu],
Zhang, Z.[Zhi],
Xiong, D.[Dehui],
Du, J.[Juan],
Liao, M.S.[Ming-Sheng],
Spatio-Temporal Series Remote Sensing Image Prediction Based on
Multi-Dictionary Bayesian Fusion,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Li, B.C.[Ben-Chong],
Yang, Y.L.[You-Long],
Complexity of concept classes induced by discrete Markov networks and
Bayesian networks,
PR(82), 2018, pp. 31-37.
Elsevier DOI
1806
Bayesian networks, Classification, Markov networks, Toric ideal,
Vapnik-Chervonenkis dimension
BibRef
Qian, S.,
Zhang, T.,
Xu, C.,
Cross-Domain Collaborative Learning via Discriminative Nonparametric
Bayesian Model,
MultMed(20), No. 8, August 2018, pp. 2086-2099.
IEEE DOI
1808
Bayes methods, belief networks, data analysis, groupware,
learning (artificial intelligence), nonparametric statistics,
multi-modality
BibRef
Tabar, V.R.[Vahid Rezaei],
Eskandari, F.[Farzad],
Salimi, S.[Selva],
Zareifard, H.[Hamid],
Finding a set of candidate parents using dependency criterion for the
K2 algorithm,
PRL(111), 2018, pp. 23-29.
Elsevier DOI
1808
Structure-learning methods in a Bayesian network is the K2 algorithm.
Bayesian network, L1-regularized Markov Blanket, Dependency criterion
BibRef
Cozman, F.G.[Fabio G.],
Mauá, D.D.[Denis D.],
The complexity of Bayesian networks specified by propositional and
relational languages,
AI(262), 2018, pp. 96-141.
Elsevier DOI
1809
Bayesian networks, Complexity theory, Relational logic,
Plate models, Probabilistic relational models
BibRef
García-Fernández, Á.F.,
Tronarp, F.,
Särkkä, S.,
Gaussian Process Classification Using Posterior Linearization,
SPLetters(26), No. 5, May 2019, pp. 735-739.
IEEE DOI
1905
approximation theory, covariance matrices, Gaussian processes,
iterative methods, covariance matrices, linearization error,
Bayesian inference
BibRef
Wang, S.F.[Shang-Fei],
Hao, L.F.[Long-Fei],
Ji, Q.A.[Qi-Ang],
Knowledge-Augmented Multimodal Deep Regression Bayesian Networks for
Emotion Video Tagging,
MultMed(22), No. 4, April 2020, pp. 1084-1097.
IEEE DOI
2004
Visualization, Tagging, Bayes methods, Feature extraction, Grammar,
Emotion recognition, Knowledge engineering,
Emotion video tagging
BibRef
Gan, Q.[Quan],
Wang, S.F.[Shang-Fei],
Hao, L.F.[Long-Fei],
Ji, Q.A.[Qi-Ang],
A Multimodal Deep Regression Bayesian Network for Affective Video
Content Analyses,
ICCV17(5123-5132)
IEEE DOI
1802
backpropagation, belief networks, image representation,
inference mechanisms, regression analysis,
Visualization
BibRef
Gao, T.[Tian],
Ji, Q.A.[Qi-Ang],
Hybrid Markov Blanket discovery,
ICPR16(1653-1658)
IEEE DOI
1705
In a Bayesian Network (BN), a target node is independent of all other
nodes given its Markov Blanket (MB).
Algorithm design and analysis, Bayes methods, Markov processes,
Random variables, Standards, Topology
BibRef
de Figueredo, C.G.[Caio G.],
Bordin, C.J.[Claudio J.],
Bruno, M.G.S.[Marcelo G. S.],
Cooperative Parameter Estimation on the Unit Sphere Using a Network
of Diffusion Particle Filters,
SPLetters(27), 2020, pp. 715-719.
IEEE DOI
2005
Manifolds, Signal processing algorithms, Particle filters,
Approximation algorithms, Random variables, Bayes methods,
particle filters
BibRef
Hirose, O.[Osamu],
A Bayesian Formulation of Coherent Point Drift,
PAMI(43), No. 7, July 2021, pp. 2269-2286.
IEEE DOI
2106
BibRef
And:
Erratum:
PAMI(43), No. 9, September 2021, pp. 3273-3273.
IEEE DOI
2108
Shape, Inference algorithms,
Bayes methods, Coherence, Matrix converters, Kernel,
fast computation
BibRef
Ye, Q.L.[Qiao-Ling],
Amini, A.A.[Arash A.],
Zhou, Q.[Qing],
Optimizing Regularized Cholesky Score for Order-Based Learning of
Bayesian Networks,
PAMI(43), No. 10, October 2021, pp. 3555-3572.
IEEE DOI
2109
Bayes methods, Simulated annealing, Tuning, Directed acyclic graph,
Annealing, Genetic algorithms, Bayesian networks,
topological sorts
BibRef
Taborsky, P.[Petr],
Vermue, L.[Laurent],
Korzepa, M.[Maciej],
Mørup, M.[Morten],
The Bayesian Cut,
PAMI(43), No. 11, November 2021, pp. 4111-4124.
IEEE DOI
2110
Bayes methods, Computational modeling, Social network services,
Image segmentation, Stochastic processes
BibRef
Petetin, Y.[Yohan],
Janati, Y.[Yazid],
Desbouvries, F.[François],
Structured Variational Bayesian Inference for Gaussian State-Space
Models With Regime Switching,
SPLetters(28), 2021, pp. 1953-1957.
IEEE DOI
2110
Computational modeling, Bayes methods, Markov processes, Switches,
Reactive power, Parameter estimation, parameter estimation
BibRef
Baggenstoss, P.M.[Paul M.],
Discriminative Alignment of Projected Belief Networks,
SPLetters(28), 2021, pp. 1963-1967.
IEEE DOI
2110
Bayes methods, Training, Entropy, Probability density function,
Data models, Costs, Cost function, Bayesian classifier,
saddle point approximation
BibRef
Alencar, A.S.C.[Alisson S. C.],
Mattos, C.L.C.[César L. C.],
Gomes, J.P.P.[Joao P. P.],
Mesquita, D.[Diego],
Bayesian Multilateration,
SPLetters(29), 2022, pp. 962-966.
IEEE DOI
2205
Bayes methods, Uncertainty, Noise measurement,
Nakagami distribution, Standards, Position measurement, navigation
BibRef
Lu, Q.[Qin],
Polyzos, K.D.[Konstantinos D.],
Li, B.C.[Bing-Cong],
Giannakis, G.B.[Georgios B.],
Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian
Process,
PAMI(45), No. 9, September 2023, pp. 11283-11296.
IEEE DOI
2309
BibRef
Sharifuzzaman-Sagar, A.S.M.,
Tanveer, J.[Jawad],
Chen, Y.[Yu],
Dang, L.M.[L. Minh],
Haider, A.[Amir],
Song, H.K.[Hyoung-Kyu],
Moon, H.[Hyeonjoon],
BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with
Quantifiable Uncertainties,
RS(16), No. 5, 2024, pp. 925.
DOI Link
2403
BibRef
He, C.C.[Chu-Chao],
Gao, X.G.[Xiao-Guang],
Guo, Z.G.[Zhi-Gao],
Structure Learning of Bayesian Networks by Finding the Optimal
Ordering,
ICPR18(177-182)
IEEE DOI
1812
Bayes methods, Heuristic algorithms, Genetic algorithms,
Search methods, Markov processes, Systems engineering and theory,
Knowledge engineering
BibRef
Oujaoura, M.,
El Ayachi, R.,
Minaoui, B.,
Fakir, M.,
Bencharef, O.,
Grouping K-Means Adjacent Regions for Semantic Image Annotation Using
Bayesian Networks,
CGiV16(243-248)
IEEE DOI
1608
belief networks
BibRef
Osogami, T.[Takayuki],
Katsuki, T.[Takayuki],
A Hierarchical Bayesian Choice Model with Visibility,
ICPR14(3618-3623)
IEEE DOI
1412
Algorithm design and analysis
BibRef
Xu, Z.[Zhen],
Srihari, S.N.[Sargur N.],
Bayesian Network Structure Learning Using Causality,
ICPR14(3546-3551)
IEEE DOI
1412
Boolean functions
BibRef
Ruiz, E.[Elías],
Sucar, L.E.[Luis Enrique],
Recognizing Visual Categories with Symbol-Relational Grammars and
Bayesian Networks,
CIARP14(540-547).
Springer DOI
1411
BibRef
Earlier:
An Object Recognition Model Based on Visual Grammars and Bayesian
Networks,
PSIVT13(349-359).
Springer DOI
1402
BibRef
Proença, P.F.[Pedro F.],
Gaspar, F.[Filipe],
Dias, M.S.[Miguel Sales],
Good Appearance and Shape Descriptors for Object Category Recognition,
ISVC13(I:385-394).
Springer DOI
1310
3D shape descriptors. efficient Naive Bayes Nearest Neighbor
BibRef
Le Capitaine, H.[Hoel],
Set-valued Bayesian inference with probabilistic equivalence,
ICPR12(2132-2135).
WWW Link.
1302
BibRef
Karayev, S.[Sergey],
Fritz, M.[Mario],
Fidler, S.[Sanja],
Darrell, T.J.[Trevor J.],
A probabilistic model for recursive factorized image features,
CVPR11(401-408).
IEEE DOI
1106
Layered representation. Learn all layers together. Recursive factorization.
Full Bayesian network (feedback/feedforward).
BibRef
Chan, A.B.[Antoni B.],
Dong, D.X.[Da-Xiang],
Generalized Gaussian process models,
CVPR11(2681-2688).
IEEE DOI
1106
unify Gaussian models.
BibRef
Filipovych, R.[Roman],
Ribeiro, E.[Eraldo],
Discovering Constrained Substructures in Bayesian Trees Using the E.M.
Algorithm,
ICIAR08(xx-yy).
Springer DOI
0806
BibRef
Ferreira, J.F.[João Filipe],
Pinho, C.[Cátia],
Dias, J.[Jorge],
Active Exploration Using Bayesian Models for Multimodal Perception,
ICIAR08(xx-yy).
Springer DOI
0806
BibRef
Tsutsui, S.[Shigeyoshi],
Cunning Ant System for Quadratic Assignment Problem with Local Search
and Parallelization,
PReMI07(269-278).
Springer DOI
0712
BibRef
Gao, R.X.[Ru-Xin],
Wu, T.F.[Tian-Fu],
Zhu, S.C.[Song-Chun],
Sang, N.[Nong],
Bayesian Inference for Layer Representation with Mixed Markov Random
Field,
EMMCVPR07(213-224).
Springer DOI
0708
BibRef
Cruz-Ramírez, N.[Nicandro],
Acosta-Mesa, H.G.[Héctor-Gabriel],
Barrientos-Martínez, R.E.[Rocío-Erandi],
Nava-Fernández, L.A.[Luis-Alonso],
Diagnosis of Chronic Idiopathic Inflammatory Bowel Disease Using
Bayesian Networks,
CIARP06(706-715).
Springer DOI
0611
BibRef
Hwang, K.S.[Keum-Sung],
Cho, S.B.[Sung-Bae],
Interactive Learning of Scene Context Extractor Using Combination of
Bayesian Network and Logic Network,
ACIVS06(1143-1150).
Springer DOI
0609
BibRef
Im, S.B.[Seung-Bin],
Cho, S.B.[Sung-Bae],
Context-Based Scene Recognition Using Bayesian Networks with
Scale-Invariant Feature Transform,
ACIVS06(1080-1087).
Springer DOI
0609
Extract features, recognize with Bayesian network.
BibRef
Matos, L.N.[Leonardo Nogueira],
de Carvalho, J.M.[Joao Marques],
Combining global and local classifiers with Bayesian network,
ICPR06(III: 1212-1215).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 952).
IEEE DOI
0609
BibRef
Palenichka, R.M.[Roman M.],
Zaremba, M.B.[Marek B.],
Perceptual Knowledge Extraction Using Bayesian Networks of Salient
Image Objects,
ICPR06(III: 1216-1219).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 953).
IEEE DOI
0609
BibRef
Zografos, V.[Vasileios],
Buxton, B.F.[Bernard F.],
Affine Invariant, Model-Based Object Recognition Using Robust Metrics
and Bayesian Statistics,
ICIAR05(407-414).
Springer DOI
0509
BibRef
Zhou, Y.[Yi],
Gu, L.[Lie],
Zhang, H.J.[Hong-Jiang],
Bayesian Tangent Shape Model:
Estimating Shape and Pose Parameters Via Bayesian Inference,
CVPR03(I: 109-116).
IEEE DOI
0307
BibRef
Grauman, K.,
Shakhnarovich, G.,
Darrell, T.J.,
A Bayesian approach to image-based visual hull reconstruction,
CVPR03(I: 187-194).
IEEE DOI
0307
BibRef
Kersten, D.[Daniel],
Object Perception: Generative Image Models and Bayesian Inference,
BMCV02(207 ff.).
Springer DOI
0303
BibRef
Garg, A.,
Pavlovic, V.,
Huang, T.S.,
Bayesian networks as ensemble of classifiers,
ICPR02(II: 779-784).
IEEE DOI
0211
BibRef
Piater, J.H.[Justus H.],
Grupen, R.A.[Roderic A.],
Feature Learning for Recognition with Bayesian Networks,
ICPR00(Vol I: 17-20).
IEEE DOI
0009
BibRef
And:
Toward Learning Visual Discrimination Strategies,
CVPR99(I: 410-415).
IEEE DOI
BibRef
And:
Learning Visual Recognition With Bayesian Networks,
UMassCS TR 99-43, March, 1999
PS File. Learning features on simple objects.
BibRef
Scalzo, F.[Fabien],
Piater, J.H.[Justus H.],
Unsupervised Learning of Dense Hierarchical Appearance Representation,
ICPR06(II: 395-398).
IEEE DOI
0609
BibRef
Earlier:
Statistical Learning of Visual Feature Hierarchies,
LCV05(III: 44-44).
IEEE DOI
0507
BibRef
Jodogne, S.,
Scalzo, F.,
Piater, J.H.,
Task-Driven Learning of Spatial Combinations of Visual Features,
LCV05(III: 48-48).
IEEE DOI
0507
BibRef
Ritter, G.,
Gallegos, M.T.,
A Bayesian Approach to Object Identification in Pattern Recognition,
ICPR00(Vol II: 418-421).
IEEE DOI
0009
BibRef
Pavlovic, V.[Vladimir],
Frey, B.J.[Brendan J.],
Huang, T.S.[Thomas S.],
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks,
CVPR99(II: 609-615).
IEEE DOI
BibRef
9900
Krebs, B.[Bjoern],
Wahl, F.M.,
Automatic Generation of Bayesian Nets for 3D Object Recognition,
ICPR98(Vol I: 126-128).
IEEE DOI
9808
Applies to related curve matching paper?
BibRef
Krebs, B.,
Burkhardt, M.,
Wahl, F.M.,
A Bayesian network for 3d object recognition in range data,
CAIP97(361-368).
Springer DOI
9709
BibRef
Krebs, B.,
Burkhardt, M.,
Korn, B.,
Handling uncertainty in 3-D object recognition using Bayesian networks,
ECCV98(II: 782).
Springer DOI
BibRef
9800
Krebs, B.,
Korn, B., and
Burkhardt, M.,
A Task Driven 3D Object Recognition System Using Bayesian Networks,
ICCV98(527-532).
IEEE DOI
BibRef
9800
Huang, T.[Timothy],
Russell, S.[Stuart],
Object Identification in a Bayesian Context,
IJCAI97(1276-1282).
BibRef
9700
Brown, C.M.[Christopher M.],
Marengoni, M.[Mauricio],
Kardaras, G.[George],
Bayes Nets for Selective Perception and Data Fusion,
SPIE(2368), 1994, pp. 117-127.
BibRef
9400
Wu, H.L.,
Cameron, A.,
A Bayesian Decision Theoretic Approach for
Adaptive Goal-Directed Sensing,
ICCV90(563-567).
IEEE DOI
BibRef
9000
Syn, M.H.[Michael H.], and
Prager, R.W.[Richard W.],
Bayesian Registration of Models Using FEM Eigenmodes,
Cambridge University1995.
Technical Report CUED/F-INFENG/TR213
Engineering Department Cambridge England.
Registration of organ images.
PS File.
BibRef
9500
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Bayesian Neural Networks .