13.3.8.9 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
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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 9701
BibRef

Kleiter, G.D.[Gernot D.],
Propagating Imprecise Probabilities in Bayesian Networks,
AI(88), No. 1-2, December 1996, pp. 143-161.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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


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

Joy, T.T., Rana, S., Gupta, S., Venkatesh, S.,
Hyperparameter tuning for big data using Bayesian optimisation,
ICPR16(2574-2579)
IEEE DOI 1705
Bayes methods, Big Data, Data models, Gaussian processes, Noise measurement, Optimization, Tuning 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

Ku, L.Y.[Li Yang], Sen, S.[Shiraj], Learned-Miller, E.G.[Erik G.], Grupen, R.A.[Roderic A.],
The Aspect Transition Graph: An Affordance-Based Model,
Affordance14(459-465).
Springer DOI 1504
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).
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Jodogne, S., Scalzo, F., Piater, J.H.,
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Graph Edit Distance, Tree-Edit Distance .


Last update:Sep 18, 2017 at 11:34:11