13.3.12.11 Bayesian Networks, Bayes Nets

Chapter Contents (Back)
Bayes Nets.
See also Bayesian Learning, Bayes Network, Bayesian Networks.

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
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Dagum, P., Chavez, R.M.,
Approximating probabilistic inference in Bayesian belief networks,
PAMI(15), No. 3, March 1993, pp. 246-255.
IEEE DOI 0401
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Olesen, K.G.,
Causal probabilistic networks with both discrete and continuous variables,
PAMI(15), No. 3, March 1993, pp. 275-279.
IEEE DOI 0401
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Gregor, J., Thomason, M.G.,
Hybrid pattern recognition using Markov networks,
PAMI(15), No. 6, June 1993, pp. 651-656.
IEEE DOI 0401
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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
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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
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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
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Kleiter, G.D.[Gernot D.],
Propagating Imprecise Probabilities in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 143-161.
Elsevier DOI 9701
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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
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van Engelen, R.A.,
Approximating Bayesian Belief Networks By Arc Removal,
PAMI(19), No. 8, August 1997, pp. 916-920.
IEEE DOI 9709
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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
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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
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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
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Earlier:
Multi-sensor fusion with Bayesian inference,
CAIP97(25-32).
Springer DOI 9709
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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
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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
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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
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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
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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
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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
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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,
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IEEE DOI Code:
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ICPR18(177-182)
IEEE DOI 1812
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CGiV16(243-248)
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ICPR14(3618-3623)
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CIARP14(540-547).
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An Object Recognition Model Based on Visual Grammars and Bayesian Networks,
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Le Capitaine, H.[Hoel],
Set-valued Bayesian inference with probabilistic equivalence,
ICPR12(2132-2135).
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IEEE DOI 1106
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CVPR11(2681-2688).
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unify Gaussian models. BibRef

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Ferreira, J.F.[João Filipe], Pinho, C.[Cátia], Dias, J.[Jorge],
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Diagnosis of Chronic Idiopathic Inflammatory Bowel Disease Using Bayesian Networks,
CIARP06(706-715).
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Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network,
ACIVS06(1143-1150).
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Im, S.B.[Seung-Bin], Cho, S.B.[Sung-Bae],
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ACIVS06(1080-1087).
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ICPR06(III: 1212-1215).
IEEE DOI 0609
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IEEE DOI 0609
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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
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IEEE DOI 0609
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BMCV02(207 ff.).
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Garg, A., Pavlovic, V., Huang, T.S.,
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ICPR02(II: 779-784).
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Piater, J.H.[Justus H.], Grupen, R.A.[Roderic A.],
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ICPR00(Vol I: 17-20).
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ICPR06(II: 395-398).
IEEE DOI 0609
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Statistical Learning of Visual Feature Hierarchies,
LCV05(III: 44-44).
IEEE DOI 0507
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Jodogne, S., Scalzo, F., Piater, J.H.,
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LCV05(III: 48-48).
IEEE DOI 0507
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ICPR00(Vol II: 418-421).
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CVPR99(II: 609-615).
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Automatic Generation of Bayesian Nets for 3D Object Recognition,
ICPR98(Vol I: 126-128).
IEEE DOI 9808
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CAIP97(361-368).
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Krebs, B., Burkhardt, M., Korn, B.,
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ECCV98(II: 782).
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Bayesian Neural Networks .


Last update:Oct 22, 2024 at 22:09:59