13.3.12.1 Evidence Theory, Combination Techniques, Optimization Techniques

Mahalanobis, P.C.,
On the Generalized Distance in Statistics,
Proc. Natl. Inst. Science(12), Calcutta, 1936, pp. 49-55. Mahalanobis Distance. BibRef 3600

Dempster, A.P.,
New Methods for Reasoning Towards Posterior Distributions Based on Sample Data,
AMS(37), No. 2, 1966. pp. 355-374. BibRef 6600

Dempster, A.P.,
Upper and Lower Probabilities Induced by a Multivalued Mapping,
AMS(38), No. 2, 1967. pp. 325-339. BibRef 6700

Dempster, A.P.,
Upper and Lower Probabilities Generalized by a Random Closed Interval,
AMS(39), No. 3, 1968. pp. 957-966. BibRef 6800

Dempster, A.P.,
Upper and Lower Probability Inferences for Families of Hypotheses with Monotone Density Ratios,
AMS(40), No. 3, 1969. pp. 953-969. BibRef 6900

Dempster, A.P.,
On Direct Probabilities,
RoyalStat(B-20), 1963, pp. 102-107. BibRef 6300

Dempster, A.P.,
On the difficulties Inherent in Fisher's Fiducial Argument,
ASAJ(59), 1964, pp. 56-66. BibRef 6400

Dempster, A.P.,
Covariance Selection,
Biometrics(28), 1972, pp. 157-175. BibRef 7200

Dempster, A.P., Laird, N.M., Rubin, D.B.,
Maximum Likelihood from Incomplete Data via the EM Algorithm,
RoyalStat(B-39), No. 1, 1977, pp. 1-38. BibRef 7700

Shafer, G.[Glenn],
A Mathematical Theory of Evidence,
Princeton Univ. PressPrinceton, NJ, 1976. Dempster-Shafer. BibRef 7600 Book This technique gives an upper and a lower bound on the possibility and a means to combine them. It is designed to eliminate the problems encountered by standard probability based systems. This is a rewording and clarification of the earlier Dempster papers. It is much more readable for a non-probability theory researcher. BibRef

Shafer, G.[Glenn], and Logan, R.,
Implementing Dempster's Rule for Hierarchical Evidence,
AI(33), No. 3, November 1987, pp. 271-298.
Elsevier DOI BibRef 8711

Shafer, G.,
Hierarchical Evidence,
CAIA85(16-21). BibRef 8500

Michalski, R.S.,
A Variable-Valued Logic System as Applied to Picture Description and Recognition,
TC(21), No. 7, July 1972, pp. 20-47. (Pages can't be right.) BibRef 7207

Voorbraak, F.[Frans],
On the justification of Dempster's rule of combination,
AI(48), No. 2, March 1991, pp. 171-197.
Elsevier DOI BibRef 9103

Hummel, R.A., and Landy, M.S.,
A Statistical Viewpoint on the Theory of Evidence,
PAMI(10), No. 2, March 1988, pp. 235-247.
IEEE DOI Dempster-Shafer. This paper discusses Dempster-Shafer theory but does not give an absolute conclusion about how best to use it or if it is worth it.
See also Mathematical Theory of Evidence, A. BibRef 8803

Hummel, R.A., and Manevitz, L.M.,
Combining Bodies of Dependent Information,
IJCAI87(1015-1017). BibRef 8700

Bowen, J.B., Mayhew, J.E.W.,
Consistency Maintenance in the Revgraph Environment,
IVC(6), No. 3, August 1988, pp. 139-150.
Elsevier DOI BibRef 8808

Barnett, J.A.,
Calculating Dempster-Shafer Plausibility,
PAMI(13), No. 6, June 1991, pp. 599-602.
IEEE DOI
See also Mathematical Theory of Evidence, A. BibRef 9106

Jain, R.C., and Haynes, S.M.,
Imprecision in Computer Vision,
Computer(15), No. 8, August 1982, pp. 39-48. Uncertainty. Survey, Uncertainty. General survey about how it is used. BibRef 8208

Sher, D.B., Hull, J.J.,
Quantifying the unimportance of prior probabilities in a computer vision problem,
ICPR90(I: 662-664).
IEEE DOI 9006
BibRef

Sher, D.B.[David B.],
Evidence Combination Using Likelihood Generators,
DARPA87(655-662). BibRef 8700
Earlier:
Evidence Combination for Vision Using Likelihood Generators,
DARPA85(255-270). The use of likelihoods from several detectors improves results. (Maybe this should be under edge detection.) BibRef

Kittler, J.V., and Hancock, E.R.,
Combining Evidence in Probabilistic Relaxation,
PRAI(3), 1989, pp. 29-51.
See also Edge-Labeling Using Dictionary-Based Relaxation. BibRef 8900

Kittler, J.V., and Föglein, J.,
On Compatibility and Support Functions in Probabilistic Relaxation,
CVGIP(34), No. 3, June 1986, pp. 257-267.
Elsevier DOI BibRef 8606
Earlier:
Contextual Decision Rules for Objects in Lattice Configurations,
ICPR84(270-272). Relaxation is compared to a compound decision rule and a number of problems arise with heuristic compatibility and support functions. The paper is only concerned with image based post processing type relaxation. BibRef

Kittler, J.V.,
Compatibility and Support Functions in Probabilistic Relaxation,
ICPR86(186-189). BibRef 8600

Kittler, J.V., and Hancock, E.R.,
Contextual Decision Rule for Image Analysis,
IVC(5), No. 2, May 1987, pp. 145-153.
Elsevier DOI BibRef 8705

Christmas, W.J., Kittler, J.V., Petrou, M.[Maria],
Structural Matching in Computer Vision Using Probabilistic Relaxation,
PAMI(17), No. 8, August 1995, pp. 749-764.
IEEE DOI BibRef 9508
Earlier: A2, A3, A1: ASSPR(471-480). Attributed Graphs. Apply to road networks. BibRef

Gilks, W., Richardson, S., Spiegelhalter, D.,
Markov Chain Monte Carlo in Practice,
Chapman and Hall1996. Markov Chain. MCMC. BibRef 9600

Ambrosio, L.,
Existence theory for a new class of variational problems,
Arch. Rational Mech. Anal.(111), No. 4, 1990, pp. 291-322.. BibRef 9000

Ambrosio, L., Fusco, N., Pallara, D.,
Functions of bounded variation and free discontinuity problems,
Clarendon PressOxford, 2000. BibRef 0001

Ambrosio, L., Tortorelli, V.M.,
Approximation of functionals depending on jumps by elliptic functionals via ..-convergence,
Comm. Pure Appl. Math.(43), No. 8, 1990, pp. 999-1036.
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef 9000

Ambrosio, L., Tortorelli, V.M.,
On the approximation of free discontinuity problems,
Boll. Un. Mat. Ital. B(6), No. 1, 1992, pp. 105-123. BibRef 9200

Christmas, W.J., Kittler, J.V., Petrou, M.,
Probabilistic Feature-Labeling Schemes: Modeling Compatibility Coefficient Distributions,
IVC(14), No. 8, August 1996, pp. 617-625.
Elsevier DOI 9609
BibRef
Earlier:
Modelling Compatibility Coefficient Distributions for Probabilistic Feature-Labelling Schemes,
BMVC95(603-612).
PDF File. BibRef

Kittler, J.V., Petrou, M., Christmas, W.J.,
A Noniterative Probabilistic Method for Contextual Correspondence Matching,
PR(31), No. 10, October 1998, pp. 1455-1468.
Elsevier DOI 9808
BibRef

Christmas, W.J., Kittler, J.V.[Josef V.], Petrou, M.[Maria],
Exploiting Temporal Context in Vision-Based Navigation,
SPIE(2736), 1996, pp. 154-161 BibRef 9600
Earlier:
Non-Iterative Contextual Correspondence Matching,
ECCV94(B:137-142).
Springer DOI BibRef

Kittler, J.V., Christmas, W.J., and Petrou, M.,
Probabilistic Relaxation for Matching Problems in Computer Vision,
ICCV93(666-673).
IEEE DOI Examples of line matching. BibRef 9300

Christmas, W.J., Kittler, J.V., Petrou, M.,
Labelling 2-D Geometric Primitives Using Probabilistic Relaxation: Reducing the Computational Requirements,
Electronic Letters(32), No. 4, 1996, pp. 312-314. BibRef 9600
And:
Matching of Road Segments Using Probabilistic Relaxation: A Hierarchical Approach,
SPIE(2304), July 1994, pp. 166-174. BibRef
And:
Matching of Road Segments Using Probabilistic Relaxation: Reducing the Computational Requirements,
SPIE(2220), April 1994, pp. 169-179. BibRef
And:
Location of Objects in a Cluttered Scene Using Probabilistic Relaxation,
AVFP94(119-128). BibRef

Kostin, A.[Alexey], Kittler, J.V.[Josef V.], Christmas, W.J.[William J.],
Object recognition by symmetrised graph matching using relaxation labelling with an inhibitory mechanism,
PRL(26), No. 3, February 2005, pp. 381-393.
Elsevier DOI 0501
BibRef

Sofer, D.,
Constraint Networks in Vision,
TC(40), 1991, pp. 1359-1367. BibRef 9100

Smets, P.,
The combination of evidence in the transferable belief model,
PAMI(12), No. 5, May 1990, pp. 447-458.
IEEE DOI 0401
BibRef

Elouedi, Z., Mellouli, K., Smets, P.,
Assessing Sensor Reliability for Multisensor Data Fusion Within the Transferable Belief Model,
SMC-B(34), No. 1, February 2004, pp. 782-787.
IEEE Abstract. 0403
How reliable is a sensor in a data fusion application. BibRef

Delmotte, F., Smets, P.,
Target Identification Based on the Transferable Belief Model Interpretation of Dempster-Shafer Model,
SMC-A(34), No. 4, July 2004, pp. 457-471.
IEEE Abstract. 0407
BibRef

Abramson, B.,
Expected-outcome: a general model of static evaluation,
PAMI(12), No. 2, February 1990, pp. 182-193.
IEEE DOI 0401
BibRef

Bhatnagar, R., Kanal, L.N.,
Structural and probabilistic knowledge for abductive reasoning,
PAMI(15), No. 3, March 1993, pp. 233-245.
IEEE DOI 0401
BibRef

Fertig, K.W., Breese, J.S.,
Probability intervals over influence diagrams,
PAMI(15), No. 3, March 1993, pp. 280-286.
IEEE DOI 0401
BibRef

Heckerman, D., Horvitz, E., Middleton, B.,
An approximate nonmyopic computation for value of information,
PAMI(15), No. 3, March 1993, pp. 292-298.
IEEE DOI 0401
BibRef

Provan, G.M., Clarke, J.R.,
Dynamic network construction and updating techniques for the diagnosis of acute abdominal pain,
PAMI(15), No. 3, March 1993, pp. 299-307.
IEEE DOI 0401
BibRef

Lee, W.T., Tenorio, M.F.,
On an asymptotically optimal adaptive classifier design criterion,
PAMI(15), No. 3, March 1993, pp. 312-318.
IEEE DOI 0401
BibRef

Sucar, L.E., Gillies, D.F.,
Probabilistic Reasoning in High-Level Vision,
IVC(12), No. 1, January-February 1994, pp. 42-60.
Elsevier DOI BibRef 9401

Caelli, T.M.[Terry M.], Dreier, A.,
Variations on the Evidence-Based Object Recognition Theme,
PR(27), No. 2, February 1994, pp. 185-204.
Elsevier DOI BibRef 9402
Earlier:
Some new techniques for evidence-based object recognition: EB-ORS1,
ICPR92(II:450-454).
IEEE DOI 9208
BibRef

Bloch, I.,
Some Aspects of Dempster-Shafer Evidence Theory for Classification of Multimodality Medical Images Taking Partial Volume Effect into Account,
PRL(17), No. 8, July 1 1996, pp. 905-919. 9608

See also Mathematical Theory of Evidence, A. BibRef

Fixsen, D., Mahler, R.P.S.,
The Modified Dempster-Shafer Approach to Classification,
SMC-A(27), No. 1, January 1997, pp. 96-104.
IEEE Top Reference. 9701
BibRef

Hand, D.J.,
Recent Advances in Error Rate Estimation,
PRL(4), 1986, pp. 335-346. BibRef 8600

Cheng, Y.Z.[Yi-Zong], Kashyap, R.L.,
A Study of Associative Evidential Reasoning,
PAMI(11), No. 6, June 1989, pp. 623-631.
IEEE DOI BibRef 8906
Earlier:
Construction and Interpretations of Formulas for Combining Evidence,
ICPR86(1226-1229). BibRef

Mogre, A., McLaren, R., Keller, J.M., Krishnapuram, R.,
Uncertainty Management for Rule-Based Systems with Applications to Image Analysis,
SMC(24), 1994, pp. 470-481. BibRef 9400

Bauer, M.,
Approximation Algorithms and Decision-Making in the Dempster-Shafer Theory of Evidence: An Empirical-Study,
ApproximateR(17), No. 2-3, August/October 1997, pp. 217-237. 9706

See also Mathematical Theory of Evidence, A. BibRef

Ménard, M.[Michel], Courboulay, V.[Vincent], Dardignac, P.A.[Pierre-André],
Possibilistic and probabilistic fuzzy clustering: unification within the framework of the non-extensive thermostatistics,
PR(36), No. 6, June 2003, pp. 1325-1342.
Elsevier DOI 0304
BibRef

Masson, M.H.[Marie-Hélène], Denoeux, T.[Thierry],
Clustering interval-valued proximity data using belief functions,
PRL(25), No. 2, January 2004, pp. 163-171.
Elsevier DOI 0401
BibRef

Félix, P., Barro, S., Marín, R.,
Fuzzy constraint networks for signal pattern recognition,
AI(148), No. 1-2, August 2003, pp. 103-140.
Elsevier DOI 0401
BibRef

Cuzzolin, F.,
Geometry of Dempster's Rule of Combination,
SMC-B(34), No. 2, April 2004, pp. 961-977.
IEEE Abstract. 0404
BibRef

Cuzzolin, F.,
Two New Bayesian Approximations of Belief Functions Based on Convex Geometry,
SMC-B(37), No. 4, August 2007, pp. 993-1008.
IEEE DOI 0707
BibRef

Boudraa, A.O.[Abdel-Ouahab], Bentabet, A.[Ayachi], Salzenstein, F.[Fabien],
Dempster-Shafer's Basic Probability Assignment Based on Fuzzy Membership Functions,
ELCVIA(4), No. 1, October 2004, pp. xx-yy.
DOI Link 0410
BibRef

Draper, B.A., Elliott, D.L., Hayes, J., Baek, K.,
EM in High-Dimensional Spaces,
SMC-B(35), No. 3, June 2005, pp. 571-577.
IEEE DOI 0508
BibRef

Pieczynski, W.[Wojciech], Benboudjema, D.[Dalila],
Multisensor triplet Markov fields and theory of evidence,
IVC(24), No. 1, 1 January 2006, pp. 61-69.
Elsevier DOI 0602
BibRef

Guo, H., Shi, W., Deng, Y.,
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory,
SMC-B(36), No. 5, October 2006, pp. 970-981.
IEEE DOI 0609
BibRef

Ghosh, D., Pados, D.A., Acharya, R., Llinas, J.,
On Dempster-Shafer and bayesian detectors,
SMC-C(36), No. 5, September 2006, pp. 688-692.
IEEE DOI 0609
BibRef

Schonfeld, D., Bouaynaya, N.,
A New Method for Multidimensional Optimization and Its Application in Image and Video Processing,
SPLetters(13), No. 8, August 2006, pp. 485-488.
IEEE DOI 0606
BibRef

Xu, G.P.[Guo-Ping], Tian, W.F.[Wei-Feng], Qian, L.[Li], Zhang, X.F.[Xiang-Fen],
A novel conflict reassignment method based on grey relational analysis (GRA),
PRL(28), No. 15, 1 November 2007, pp. 2080-2087.
Elsevier DOI 0711
Evidence theory; Belief functions; Grey relational analysis; Conflict reassignment; Target recognition; Reliability evaluation BibRef

Olsson, C.[Carl], Eriksson, A.P.[Anders P.], Kahl, F.[Fredrik],
Improved spectral relaxation methods for binary quadratic optimization problems,
CVIU(112), No. 1, October 2008, pp. 3-13.
Elsevier DOI 0810
BibRef
Earlier:
Solving Large Scale Binary Quadratic Problems: Spectral Methods vs. Semidefinite Programming,
CVPR07(1-8).
IEEE DOI 0706
BibRef
And:
Efficient Optimization for L-inf-problems using Pseudoconvexity,
ICCV07(1-8).
IEEE DOI 0710
For relaxation implementations. Quadratic binary optimization; Spectral relaxation; Image partitioning; Subgraph matching; Trust region problem; Semidefinite programming; Discrete optimization; Binary restoration BibRef

Olsson, C.[Carl], Eriksson, A.P.[Anders P.], Hartley, R.I.[Richard I.],
Outlier removal using duality,
CVPR10(1450-1457).
IEEE DOI 1006
In large scale reconstructions. BibRef

Olsson, C.[Carl], Eriksson, A.P.[Anders P.],
Triangulating a Plane,
SCIA11(13-23).
Springer DOI 1105
BibRef

Olsson, C.[Carl], Eriksson, A.P.[Anders P.],
Solving quadratically constrained geometrical problems using lagrangian duality,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Eriksson, A.P.[Anders P.], Olsson, C.[Carl], Kahl, F.[Fredrik],
Efficiently Solving the Fractional Trust Region Problem,
ACCV07(II: 796-805).
Springer DOI 0711
BibRef

Hartley, R.I.[Richard I.], Kahl, F.[Fredrik],
Global Optimization through Rotation Space Search,
IJCV(82), No. 1, April 2009, pp. xx-yy.
Springer DOI 0902
BibRef
Earlier:
Global Optimization through Searching Rotation Space and Optimal Estimation of the Essential Matrix,
ICCV07(1-8).
IEEE DOI 0710
Branch-and-bound search over rotations. BibRef

Mantrach, A.[Amin], Yen, L.[Luh], Callut, J.[Jerome], Francoisse, K.[Kevin], Shimbo, M.[Masashi], Saerens, M.[Marco],
The Sum-over-Paths Covariance Kernel: A Novel Covariance Measure between Nodes of a Directed Graph,
PAMI(32), No. 6, June 2010, pp. 1112-1126.
IEEE DOI 1004
BibRef

Senelle, M., Garcia-Diez, S., Mantrach, A., Shimbo, M., Saerens, M., Fouss, F.,
The Sum-over-Forests Density Index: Identifying Dense Regions in a Graph,
PAMI(36), No. 6, June 2014, pp. 1268-1274.
IEEE DOI 1406
Correlation BibRef

Kanatani, K.[Kenichi], Sugaya, Y.[Yasuyuki], Niitsuma, H.[Hirotaka],
Optimization without Minimization Search: Constraint Satisfaction by Orthogonal Projection with Applications to Multiview Triangulation,
IEICE(E93-D), No. 10, October 2010, pp. 2836-2845.
WWW Link. 1011
BibRef

Esser, E.[Ernie], Zhang, X.[Xiaoqun], Chan, T.F.[Tony F.],
A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization In Imaging Science,
SIIMS(3), No. 4, 2010, pp. 1015-1046.
WWW Link.
DOI Link convex optimization; total variation minimization; primal-dual methods; operator splitting; L_1 basis pursuit BibRef 1000

Enqvist, O.[Olof], Kahl, F.[Fredrik], Olsson, C.[Carl], Astrom, K.[Kalle],
Global Optimization For One-Dimensional Structure And Motion Problems,
SIIMS(3), No. 4, 2010, pp. 1075-1095.
WWW Link.
DOI Link structure and motion; one-dimensional vision; simultaneous localization and mapping; geometry BibRef 1000

Astrom, K.[Kalle], Enqvist, O.[Olof], Olsson, C.[Carl], Kahl, F.[Fredrik], Hartley, R.I.[Richard I.],
An L-inf to Structure and Motion Problems in 1D-Vision,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Olsson, C.[Carl], Enqvist, O.[Olof],
Stable Structure from Motion for Unordered Image Collections,
SCIA11(524-535).
Springer DOI 1105
BibRef

Becker, S.[Stephen], Bobin, J.[Jerome], Candes, E.J.[Emmanuel J.],
Nesta: A Fast And Accurate First-Order Method For Sparse Recovery,
SIIMS(4), No. 1, 2011, pp. 1-39.
WWW Link.
DOI Link Nesterov's method; smooth approximations of nonsmooth functions; L_1 minimization; duality in convex optimization; continuation methods; compressed sensing; total-variation minimization BibRef 1100

Hager, W.W.[William W.], Phan, D.T.[Dzung T.], Zhang, H.C.[Hong-Chao],
Gradient-Based Methods For Sparse Recovery,
SIIMS(4), No. 1, 2011, pp. 146-165.
WWW Link.
DOI Link sparse reconstruction by separable approximation; iterative shrinkage thresholding algorithm; sparse recovery; sublinear convergence; linear convergence; image reconstruction; denoising; compressed sensing; nonsmooth optimization; nonmonotone convergence; BB method BibRef 1100

Liu, M.[Meng], Chen, Y.M.[Yun-Mei], Ouyang, Y.Y.[Yu-Yuan], Ye, X.J.[Xiao-Jing], Huang, F.[Feng],
An enhanced approach for simultaneous image reconstruction and sensitivity map estimation in partially parallel imaging,
ICIP13(2314-2318)
IEEE DOI 1402
SENSE BibRef

Yashtini, M.[Maryam], Hager, W.W.[William W.], Chen, Y.M.[Yun-Mei], Ye, X.J.[Xiao-Jing],
Partially parallel image reconstruction using sensitivity encoding,
ICIP12(2077-2080).
IEEE DOI 1302
BibRef

Felzenszwalb, P.F.[Pedro F.], McAuley, J.J.[Julian J.],
Fast Inference with Min-Sum Matrix Product,
PAMI(33), No. 12, December 2011, pp. 2549-2554.
IEEE DOI 1110
MAP inference on graphs. Rather than the N^3 limit, a N^2logN time. BibRef

Wickramarathne, T.L., Premaratne, K., Murthi, M.N.,
Toward Efficient Computation of the Dempster-Shafer Belief Theoretic Conditionals,
Cyber(43), No. 2, April 2013, pp. 712-724.
IEEE DOI 1303
BibRef

Rusu, C.,
Design of Incoherent Frames via Convex Optimization,
SPLetters(20), No. 7, 2013, pp. 673-676.
IEEE DOI 1307
incoherent frames; Grassmannian frames BibRef

Lu, S.F.[Shao-Feng], Hillmansen, S., Ho, T.K., Roberts, C.,
Single-Train Trajectory Optimization,
ITS(14), No. 2, 2013, pp. 743-750.
IEEE DOI 1307
ant colony optimization algorithm BibRef

Horng, S.C., Lin, S.Y., Lee, L.H., Chen, C.H.,
Memetic Algorithm for Real-Time Combinatorial Stochastic Simulation Optimization Problems With Performance Analysis,
Cyber(43), No. 5, 2013, pp. 1495-1509.
IEEE DOI 1309
Real-time suboptimal solution for large optimization problems. BibRef

Zhang, X.Y.[Xu-Yao], Yang, P.P.[Pei-Pei], Zhang, Y.M.[Yan-Ming], Huang, K.Z.[Kai-Zhu], Liu, C.L.[Cheng-Lin],
Combination of Classification and Clustering Results with Label Propagation,
SPLetters(21), No. 5, May 2014, pp. 610-614.
IEEE DOI 1404
graph theory BibRef

Lu, Y.[Yao], Huang, K.Z.[Kai-Zhu], Liu, C.L.[Cheng-Lin],
A fast projected fixed-point algorithm for large graph matching,
PR(60), No. 1, 2016, pp. 971-982.
Elsevier DOI 1609
Graph matching BibRef

Fu, B.[Bin], Wang, Z.H.[Zhi-Hai], Xu, G.D.[Guan-Dong], Cao, L.B.[Long-Bing],
Multi-label learning based on iterative label propagation over graph,
PRL(42), No. 1, 2014, pp. 85-90.
Elsevier DOI 1404
Multi-label learning BibRef

Kim, K.H.[Kye-Hyeon], Choi, S.J.[Seung-Jin],
Label propagation through minimax paths for scalable semi-supervised learning,
PRL(45), No. 1, 2014, pp. 17-25.
Elsevier DOI 1407
Label propagation BibRef

Tar, P.[Paul], Thacker, N.A.[Neil A.],
Linear Poisson Models: A Pattern Recognition Solution to the Histogram Composition Problem,
BMVA(2014), No. 1, 2014, pp. 1-22.
PDF File. 1408
BibRef

Besse, F.[Frederic], Rother, C.[Carsten], Fitzgibbon, A.W.[Andrew W.], Kautz, J.[Jan],
PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation,
IJCV(110), No. 1, October 2014, pp. 2-13.
Springer DOI
WWW Link. 1410
BibRef
Earlier: BMVC12(132).
DOI Link 1301
Award, BMVC, Best Impact. BibRef

Hornácek, M.[Michael], Besse, F.[Frederic], Kautz, J.[Jan], Fitzgibbon, A.W.[Andrew W.], Rother, C.[Carsten],
Highly Overparameterized Optical Flow Using PatchMatch Belief Propagation,
ECCV14(III: 220-234).
Springer DOI 1408
BibRef

Yazidi, A.[Anis], Granmo, O.C.[Ole-Christoffer], Oommen, B.J.[B. John], Goodwin, M.,
A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme,
Cyber(44), No. 11, November 2014, pp. 2202-2220.
IEEE DOI 1411
Optimization process. Determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. BibRef

Yazidi, A.[Anis], Oommen, B.J.[B. John], Goodwin, M.,
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments,
Cyber(47), No. 7, July 2017, pp. 1604-1617.
IEEE DOI 1706
Bayes methods, Learning automata, Redundancy, Reliability theory, Sensor fusion, Learning automata (LA), sensor, fusion BibRef

Yazidi, A.[Anis], Pinto-Orellana, M.A.[Marco Antonio], Hammer, H.[Hugo], Mirtaheri, P.[Peyman], Herrera-Viedma, E.[Enrique],
Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics,
Cyber(52), No. 1, January 2022, pp. 16-24.
IEEE DOI 2201
Sociology, Statistics, Reliability theory, Sensor fusion, Convergence, Mathematical model, Distributed learning, sensor fusion BibRef

Yazidi, A.[Anis], Oommen, B.J.[B. John], Horn, G.[Geir], Granmo, O.C.[Ole-Christoffer],
Stochastic discretized learning-based weak estimation: A novel estimation method for non-stationary environments,
PR(60), No. 1, 2016, pp. 430-443.
Elsevier DOI 1609
Weak estimators BibRef

Denoux, T., El Zoghby, N., Cherfaoui, V., Jouglet, A.,
Optimal Object Association in the Dempster-Shafer Framework,
Cyber(44), No. 12, December 2014, pp. 2521-2531.
IEEE DOI 1412
belief maintenance BibRef

Roy, P.C., Islam, M.M., Murase, K., Yao, X.,
Evolutionary Path Control Strategy for Solving Many-Objective Optimization Problem,
Cyber(45), No. 4, April 2015, pp. 702-715.
IEEE DOI 1503
Euclidean distance BibRef

Nayeem, M.A., Islam, M.M., Yao, X.,
Solving Transit Network Design Problem Using Many-Objective Evolutionary Approach,
ITS(20), No. 10, October 2019, pp. 3952-3963.
IEEE DOI 1910
Optimization, Linear programming, Urban areas, Evolutionary computation, Complexity theory, Local government, evolutionary algorithm BibRef

Gong, W., Cai, Z., Liang, D.,
Adaptive Ranking Mutation Operator Based Differential Evolution for Constrained Optimization,
Cyber(45), No. 4, April 2015, pp. 716-727.
IEEE DOI 1503
Benchmark testing BibRef

Wei, X., Tao, Z., Zhang, C., Cao, X.,
Structured Saliency Fusion Based on Dempster-Shafer Theory,
SPLetters(22), No. 9, September 2015, pp. 1345-1349.
IEEE DOI 1503
Benchmark testing BibRef

Gould, S.,
Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields,
PAMI(37), No. 7, July 2015, pp. 1336-1346.
IEEE DOI 1506
Computational modeling BibRef

Komodakis, N.[Nikos], Xiang, B., Paragios, N.[Nikos],
A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models,
PAMI(37), No. 7, July 2015, pp. 1425-1441.
IEEE DOI 1506
Analytical models BibRef

Werner, T.,
Marginal Consistency: Upper-Bounding Partition Functions over Commutative Semirings,
PAMI(37), No. 7, July 2015, pp. 1455-1468.
IEEE DOI 1506
Abstracts BibRef

Liang, Y.C.[Yu-Chen], Zhang, Z.[Zhao], Jiang, W.M.[Wei-Ming], Zhao, M.B.[Ming-Bo], Li, F.Z.[Fan-Zhang],
Bilinear Embedding Label Propagation: Towards Scalable Prediction of Image Labels,
SPLetters(22), No. 12, December 2015, pp. 2411-2415.
IEEE DOI 1512
image classification BibRef

Çakmak, B., Urup, D.N., Meyer, F., Pedersen, T., Fleury, B.H., Hlawatsch, F.,
Cooperative Localization for Mobile Networks: A Distributed Belief Propagation: Mean Field Message Passing Algorithm,
SPLetters(23), No. 6, June 2016, pp. 828-832.
IEEE DOI 1606
Gaussian processes BibRef

Liu, Y.L.[Yu-Lang], Zhao, Z.Q.[Zhi-Qin], Yang, Y.H.[Yao-Hui], Wang, B.W.[Bing-Wen], Zhu, X.Z.[Xiao-Zhang], Nie, Z.P.[Zai-Ping], Liu, Q.H.[Qing Huo],
A Frequency-Hopping Subspace-Based Optimization Method for Reconstruction of 2-D Large Uniaxial Anisotropic Scatterers With TE Illumination,
GeoRS(54), No. 10, October 2016, pp. 6091-6099.
IEEE DOI 1610
anisotropic media BibRef

Ke, X.[Xiao], Zhou, M.K.[Ming-Ke], Niu, Y.Z.[Yu-Zhen], Guo, W.Z.[Wen-Zhong],
Data equilibrium based automatic image annotation by fusing deep model and semantic propagation,
PR(71), No. 1, 2017, pp. 60-77.
Elsevier DOI 1707
SAE: stacked auto-encoder. BibRef

Knoll, C., Mehta, D., Chen, T., Pernkopf, F.,
Fixed Points of Belief Propagation: An Analysis via Polynomial Homotopy Continuation,
PAMI(40), No. 9, September 2018, pp. 2124-2136.
IEEE DOI 1808
Mathematical model, Convergence, Belief propagation, Graphical models, Probabilistic logic, Computational modeling, dynamical equations BibRef

Belle, V.[Vaishak], Levesque, H.J.[Hector J.],
Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems,
AI(262), 2018, pp. 189-221.
Elsevier DOI 1809
Knowledge representation, Reasoning about action, Reasoning about knowledge, Reasoning about uncertainty, Cognitive robotics. Reasoning about the results after the animal is not there. BibRef

Yu, L.[Lei], Yang, T.Y.[Tian-Yu], Chan, A.B.[Antoni B.],
Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference,
PAMI(41), No. 6, June 2019, pp. 1323-1337.
IEEE DOI 1905
Mixture models, Approximation algorithms, Clustering algorithms, Bayes methods, Inference algorithms, Gaussian mixture model, recursive Bayesian filtering BibRef

Ayoobi, H.[Hamed], Rezaeian, M.[Mehdi],
Swift distance transformed belief propagation using a novel dynamic label pruning method,
IET-IPR(14), No. 9, 20 July 2020, pp. 1822-1831.
DOI Link 2007
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Pereyra, M.[Marcelo], Mieles, L.V.[Luis Vargas], Zygalakis, K.C.[Konstantinos C.],
Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method,
SIIMS(13), No. 2, 2020, pp. 905-935.
DOI Link 2007
MCMC BibRef

Holden, M.[Matthew], Pereyra, M.[Marcelo], Zygalakis, K.C.[Konstantinos C.],
Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks,
SIIMS(15), No. 2, 2022, pp. 892-924.
DOI Link 2207
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Shen, R.B.[Ruo-Bing], Tang, B.[Bo], Lodi, A.[Andrea], Tramontani, A.[Andrea], Ben Ayed, I.[Ismail],
An ILP Model for Multi-Label MRFs With Connectivity Constraints,
IP(29), 2020, pp. 6909-6917.
IEEE DOI 2007
Computational modeling, Optimization, Semantics, Machine learning, Labeling, Particle separators, Markov random fields BibRef

Kárný, M.[Miroslav],
On assigning probabilities to new hypotheses,
PRL(150), 2021, pp. 170-175.
Elsevier DOI 2109
Minimum relative-entropy principle, Prior probability, Hypothesis BibRef

Knoll, C.[Christian], Weller, A.[Adrian], Pernkopf, F.[Franz],
Self-Guided Belief Propagation: A Homotopy Continuation Method,
PAMI(45), No. 4, April 2023, pp. 5139-5157.
IEEE DOI 2303
Computational modeling, Belief propagation, Probabilistic logic, Convergence, Graphical models, Couplings, Random variables, inference algorithms BibRef

Liu, R.S.[Ri-Sheng], Liu, X.[Xuan], Zeng, S.Z.[Shang-Zhi], Zhang, J.[Jin], Zhang, Y.X.[Yi-Xuan],
Value-Function-Based Sequential Minimization for Bi-Level Optimization,
PAMI(45), No. 12, December 2023, pp. 15930-15948.
IEEE DOI 2311
BibRef

Zhang, Z.[Zhuo], Wang, H.F.[Hong-Fei], Geng, J.[Jie], Deng, X.Y.[Xin-Yang], Jiang, W.[Wen],
A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory,
MultMed(26), 2024, pp. 4998-5013.
IEEE DOI 2404
Uncertainty, Training, Data augmentation, Artificial neural networks, Training data, Task analysis, deep neural network BibRef

Yang, Y.D.[Yi-Ding], Qiu, J.Y.[Jia-Yan], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng], Wang, X.C.[Xin-Chao],
Learning Propagation Rules for Attribution Map Generation,
ECCV20(XX:672-688).
Springer DOI 2011
BibRef

Nijkamp, E.[Erik], Pang, B.[Bo], Han, T.[Tian], Zhou, L.Q.[Lin-Qi], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference,
ECCV20(VI:361-378).
Springer DOI 2011
BibRef

Eyzaguirre, C.[Cristóbal], Soto, Á.[Álvaro],
Differentiable Adaptive Computation Time for Visual Reasoning,
CVPR20(12814-12822)
IEEE DOI 2008
Computational modeling, Adaptation models, Visualization, Pipelines, Mathematical model, Computer architecture, Cognition BibRef

Knöbelreiter, P., Sormann, C., Shekhovtsov, A., Fraundorfer, F., Pock, T.,
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems,
CVPR20(7897-7906)
IEEE DOI 2008
Computational modeling, Belief propagation, Semantics, Task analysis, Training, Schedules, Neural networks BibRef

Millane, R.P., Taylor, J.T., Arnal, R.D., Wojtas, D.H., Clare, R.M.,
Iterative projection algorithms for solving constraint satisfaction problems: Effect of constraint convexity,
IVCNZ19(1-5)
IEEE DOI 2004
concave programming, constraint satisfaction problems, constraint theory, image processing, inverse problems, optimization BibRef

Ghandeharioun, A.[Asma], Eoff, B.[Brian], Jou, B.[Brendan], Picard, R.[Rosalind],
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias,
VXAI19(4202-4206)
IEEE DOI 2004
How to learn when human annotation disagrees. learning (artificial intelligence), Monte Carlo methods, proxy calibration, disambiguate annotator, data bias, Monte-Carlo-Dropout BibRef

Trusheim, F.[Felix], Condurache, A.[Alexandru], Mertins, A.[Alfred],
Boosting Black-Box Variational Inference by Incorporating the Natural Gradient,
ICPR18(19-24)
IEEE DOI 1812
Inference algorithms, Approximation algorithms, Mathematical model, Monte Carlo methods, Manifolds, Stochastic processes BibRef

Wannenwetsch, A.S.[Anne S.], Kiefel, M.[Martin], Gehler, P.V.[Peter V.], Roth, S.[Stefan],
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice,
GCPR19(345-359).
Springer DOI 1911
BibRef

Plötz, T.[Tobias], Wannenwetsch, A.S.[Anne S.], Roth, S.[Stefan],
Stochastic Variational Inference with Gradient Linearization,
CVPR18(1566-1575)
IEEE DOI 1812
Stochastic processes, Mathematical model, Uncertainty, Optimization, Computational modeling, Standards BibRef

Laude, E., Lange, J., Schüpfer, J., Domokos, C., Leal-Taixé, L., Schmidt, F.R., Andres, B., Cremers, D.,
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs,
CVPR18(1614-1624)
IEEE DOI 1812
Task analysis, Optimization, Labeling, Training, Computational modeling, Convex functions, Image segmentation BibRef

Zach, C.[Christopher],
Limited-Memory Belief Propagation via Nested Optimization,
EMMCVPR17(517-532).
Springer DOI 1805
BibRef

Badami, I., Tom, M., Mathias, M., Leibe, B.,
3D Semantic Segmentation of Modular Furniture Using rjMCMC,
WACV17(64-72)
IEEE DOI 1609
Image edge detection, Image segmentation, Optimization, Proposals, Semantics, Shape, Three-dimensional, displays BibRef

Zhang, R.N.[Ruo-Nan], Wang, W.[Wenmin],
An MCMC-based prior sub-hypergraph matching in presence of outliers,
ICPR16(799-804)
IEEE DOI 1705
Benchmark testing, Markov processes, Optimization, Probability distribution, Proposals, Robustness, Tensile, stress BibRef

Zhang, M.H.[Miao-Hua], Gao, Y.S.[Yong-Sheng], Sun, C.M.[Chang-Ming], La Salle, J., Liang, J.,
Robust tensor factorization using maximum correntropy criterion,
ICPR16(4184-4189)
IEEE DOI 1705
Covariance matrices, Kernel, Linear programming, Optimization, Principal component analysis, Robustness, Tensile, stress BibRef

Nguyen, M.[Minh], Yan, W.Q.[Wei Qi], Gong, R.[Rui], Delmas, P.[Patrice],
Toward a real-time belief propagation stereo reconstruction for computers, robots, and beyond,
ICVNZ15(1-6)
IEEE DOI 1701
Markov processes BibRef

Cadoni, S.[Sara], Chouzenoux, E.[Emilie], Pesquet, J.C.[Jean-Christophe], Chaux, C.[Caroline],
A block parallel majorize-minimize memory gradient algorithm,
ICIP16(3194-3198)
IEEE DOI 1610
Parallel implementation of optimization. BibRef

Tang, K.[Keke], Zhao, Z.[Zhe], Chen, X.P.[Xiao-Ping],
Label Propagation for Large Scale 3D Indoor Scenes,
ISVC15(I: 253-264).
Springer DOI 1601
BibRef

Zhang, Z.[Zhao], Jiang, W.M.[Wei-Ming], Li, F.Z.[Fan-Zhang], Zhang, L.[Li], Zhao, M.B.[Ming-Bo], Jia, L.[Lei],
Projective Label Propagation by Label Embedding,
CAIP15(II:470-481).
Springer DOI 1511
BibRef

Xiong, X.[Xuehan], de la Torre, F.[Fernando],
Global supervised descent method,
CVPR15(2664-2673)
IEEE DOI 1510
BibRef

Tabib, R.A., Patil, U., Ganihar, S.A., Trivedi, N., Mudenagudi, U.,
Decision fusion for robust horizon estimation using Dempster Shafer Combination Rule,
NCVPRIPG13(1-4)
IEEE DOI 1408
Gaussian processes BibRef

Herman, G.T.[Gabor T.],
Superiorization for Image Analysis,
IWCIA14(1-7).
Springer DOI 1405
Constrained optimization. BibRef

Leng, B.[Biao], Zhang, X.Y.[Xiang-Yang], Yao, M.[Ming], Xiong, Z.[Zhang],
3D Object Classification Using Deep Belief Networks,
MMMod14(II: 128-139).
Springer DOI 1405
BibRef

Carneiro, G., Liao, Z.B.[Zhi-Bin], Chin, T.J.[Tat-Jun],
Closed-Loop Deep Vision,
DICTA13(1-8)
IEEE DOI 1402
belief networks BibRef

Olsson, C.[Carl], Ulen, J.[Johannes], Boykov, Y.Y.[Yuri Y.], Kolmogorov, V.[Vladimir],
Partial Enumeration and Curvature Regularization,
ICCV13(2936-2943)
IEEE DOI 1403
BibRef

Gupta, M.D.[Mithun Das], Kumar, S.[Sanjeev],
Non-convex P-Norm Projection for Robust Sparsity,
ICCV13(1593-1600)
IEEE DOI 1403
BibRef

Yang, H.C.[Hai-Chuan], Bai, X.[Xiao], Liu, C.T.[Chun-Tian], Zhou, J.[Jun],
Label propagation hashing based on p-stable distribution and coordinate descent,
ICIP13(2674-2678)
IEEE DOI 1402
Coordinate descent;Hashing;Image retrieval;p-stable distribution BibRef

Nascimento, J.C.[Jacinto C.], Barão, M.[Miguel], Marques, J.S.[Jorge S.], Lemos, J.M.[João M.],
Efficient Optimization Algorithm for Space-Variant Mixture of Vector Fields,
IbPRIA13(79-88).
Springer DOI 1307
BibRef

Kanatani, K.[Kenichi],
Statistical Optimization for Geometric Estimation: Minimization vs. Non-minimization,
ICPR14(1-8)
IEEE DOI 1412
BibRef
Earlier:
Optimization Techniques for Geometric Estimation: Beyond Minimization,
SSSPR12(11-30).
Springer DOI 1211
Cost function BibRef

Kuznetsova, A.[Alina], Pons-Moll, G.[Gerard], Rosenhahn, B.[Bodo],
PCA-enhanced Stochastic Optimization Methods,
DAGM12(377-386).
Springer DOI 1209
BibRef

Sun, J.[Jun], Li, H.D.[Hong-Dong], He, X.M.[Xu-Ming],
Analysis on Tree Structure Selection for MRF Inference in Low-level Vision,
DICTA11(66-71).
IEEE DOI 1205
BibRef

Goodman, N.D.[Noah D.],
Learning and the language of thought,
SIG11(694).
IEEE DOI 1201
Invited talk. BibRef

Gamal-Eldin, A.[Ahmed], Descombes, X.[Xavier], Charpiat, G.[Guillaume], Zerubia, J.B.[Josiane B.],
A fast Multiple Birth and Cut algorithm using belief propagation,
ICIP11(2813-2816).
IEEE DOI 1201
BibRef

Bouchon-Meunier, B.[Bernadette],
Similarity and prototype: Two key issues in perceptive and subjective information,
EUVIP11(222).
IEEE DOI 1110
BibRef

Chen, Q.A.[Qi-Ang], Song, Z.[Zheng], Dong, J., Huang, Z.Y.[Zhong-Yang], Hua, Y.[Yang], Yan, S.C.[Shui-Cheng],
Contextualizing Object Detection and Classification,
PAMI(37), No. 1, January 2015, pp. 13-27.
IEEE DOI 1412
BibRef
Earlier: A2, A1, A4, A5, A6:
Contextualizing object detection and classification,
CVPR11(1585-1592).
IEEE DOI 1106
Computational modeling. Boost classification by using results from a different task. Context-SVM. Object classification and extraction tasks so they are related. BibRef

Lasowski, R.[Ruxandra], Tevs, A.[Art], Wand, M.[Michael], Seidel, H.P.[Hans-Peter],
Wavelet belief propagation for large scale inference problems,
CVPR11(1921-1928).
IEEE DOI 1106
BibRef

Xiao, P.D.[Peng-Dong], Barnes, N.M.[Nick M.], Lieby, P.[Paulette], Caetano, T.S.[Tiberio S.],
Sparse Update for Loopy Belief Propagation: Fast Dense Registration for Large State Spaces,
DICTA10(546-551).
IEEE DOI 1012
BibRef

Ogawara, K.[Koichi],
Approximate Belief Propagation by Hierarchical Averaging of Outgoing Messages,
ICPR10(1368-1372).
IEEE DOI 1008
BibRef

Woodford, O.J.[Oliver J.], Rother, C.[Carsten], Kolmogorov, V.[Vladimir],
A global perspective on MAP inference for low-level vision,
ICCV09(2319-2326).
IEEE DOI 0909
Maximum a posteriori framework rather than MRF probability model. Create Marginal Probability Field -- a MRF generalization. BibRef

Cao, L.L.[Liang-Liang], Luo, J.B.[Jie-Bo], Liang, F.[Feng], Huang, T.S.[Thomas S.],
Heterogeneous feature machines for visual recognition,
ICCV09(1095-1102).
IEEE DOI 0909
Model and compare heterogeneous features. BibRef

Huang, Y.[Yihu], Zhang, G.[Genmin], Wang, J.[Jinli],
An Optimization Dijkstra Algorithm Based on Two-Function Limitation Strategy,
CISP09(1-4).
IEEE DOI 0910
BibRef

Wu, J.C.[Jin-Cheng],
APSK Optimization in the Presence of Phase Noise,
CISP09(1-5).
IEEE DOI 0910
BibRef

Zhang, L.[Liang], Huang, S.X.[Si-Xun],
Generalized Variational Optimization Analysis for Improving Scatterometer Surface Wind Field,
CISP09(1-3).
IEEE DOI 0910
BibRef

Jin, W.G.[Wen-Guang], Zhang, B.[Bin], Zhu, D.Q.[De-Qing], Hu, K.L.[Kai-Liang],
Multilevel Optimization of DSP Based SPEEX Decoder,
CISP09(1-4).
IEEE DOI 0910
BibRef

Sun, F.R.[Feng-Rong], Zhang, M.Q.A.[Ming-Qi-Ang],
Numerical Method of an Orthogonal Array Optimization,
CISP09(1-3).
IEEE DOI 0910
BibRef

Matsumoto, M.,
Parameter Optimization of Median epsilon-Filter Based on Correlation Maximization,
CISP09(1-5).
IEEE DOI 0910
BibRef

Yang, R.G.[Rong-Gen], Ren, M.W.[Ming-Wu],
Improved Non Convex Optimization Algorithm for Reconstruction of Sparse Signals,
CISP09(1-5).
IEEE DOI 0910
BibRef

Liang, C.K.[Chia-Kai], Cheng, C.C.[Chao-Chung], Lai, Y.C.[Yen-Chieh], Chen, L.G.[Liang-Gee], Chen, H.H.[Homer H.],
Hardware-efficient belief propagation,
CVPR09(80-87).
IEEE DOI 0906
Split NRF into tiles with minimal interaction between them. BibRef

Tipwai, P., Madarasmi, S.,
A dual belief propagation method for shape recognition,
CIIP09(88-95).
IEEE DOI 0903
BibRef

Klinker, G.[Gudrun],
SudokuVis How to Explore Relationships of Mutual Exclusion,
ISVC08(II: 55-64).
Springer DOI 0812
BibRef

Chandraker, M.[Manmohan], Kriegman, D.J.[David J.],
Globally optimal bilinear programming for computer vision applications,
CVPR08(1-8).
IEEE DOI 0806
Apply to: exemplar-based face reconstruction and non-rigid structure from motion BibRef

Agarwal, S.[Sameer], Snavely, N.[Noah], Seitz, S.M.[Steven M.],
Fast algorithms for L-inf problems in multiview geometry,
CVPR08(1-8).
IEEE DOI 0806
Optimization problems.
See also Efficient Optimization for L-inf-problems using Pseudoconvexity.
See also Modeling the World from Internet Photo Collections. BibRef

Leordeanu, M.[Marius], Hebert, M.[Martial],
Smoothing-based Optimization,
CVPR08(1-8).
IEEE DOI 0806
Search for maximum in scale space. BibRef

Yu, T.L.[Tian-Li], Lin, R.S.[Ruei-Sung], Super, B.[Boaz], Tang, B.[Bei],
Efficient Message Representations for Belief Propagation,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Kotb, Y.T., Beauchemin, S.S., Barron, J.L.,
Petri Net-Based Cooperation In Multi-Agent Systems,
CRV07(123-130).
IEEE DOI 0705
BibRef

Gangaputra, S.[Sachin], Geman, D.[Donald],
Self-normalized linear tests,
CVPR04(II: 616-622).
IEEE DOI 0408
Find the threshold (with changes in illumination) to do the linear combination. BibRef

Sudderth, E.B.[Erik B.], Mandel, M.I.[Michael I.], Freeman, W.T.[William T.], Willsky, A.S.[Alan S.],
Visual Hand Tracking Using Nonparametric Belief Propagation,
GenModel04(189).
IEEE DOI 0406
BibRef
And:
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propogation,
NIPS04(xx-yy).
See also Describing Visual Scenes Using Transformed Objects and Parts. BibRef

Sudderth, E.B.[Erik B.], Ihler, A.T.[Alexander T.], Freeman, W.T.[William T.], Willsky, A.S.[Alan S.],
Nonparametric belief propagation,
CVPR03(I: 605-612).
IEEE DOI 0307
BibRef
Earlier:
Nonparametric Belief Propagation and Facial Appearance Estimation,
MIT AIMAIM-2002-020, December 2002.
WWW Link. Drawing on ideas from regularized particle filters and belief propagation (BP), this paper develops a nonparametric belief propagation (NBP) algorithm applicable to general graphs. 0306
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Orr, M.J.L.[Mark J.L.], Fisher, R.B.[Robert B.], Hallam, J.[John],
Computing with Uncertainty: Intervals versus Probabilities,
BMVC91(351-354).
PDF File. 9109
BibRef Edinburgh BibRef

Waite, M., Orr, M., Fisher, R.B., Hallam, J.,
Statistical Partial Constraints for 3D Model Matching and Pose Estimation Problems,
BMVC93(105-114).
PDF File. BibRef 9300 Edinburgh BibRef

Murphy, R.R.[Robin R.], Hawkins, D.K.[Dale K.], Schoppers, M.J.[Marcel J.],
Reactive Combination of Belief Over Time Using Direct Perception,
IJCAI97(1353-1359). BibRef 9700

Semmar, N.,
Applying contextual constraints to extract symbolic representation for image understanding,
CIAP95(721-730).
Springer DOI 9509
BibRef

Besserer, B., Estable, S., Ulmer, B.,
Multiple knowledge sources and evidential reasoning for shape recognition,
ICCV93(624-631).
IEEE DOI 0403
Uncertainty handling, combining, and propagation form the heart of the method. BibRef

Qian, J., and Ehrich, R.W.,
A Framework for Uncertainty Reasoning in Hierarchical Visual Evidence Space,
ICPR90(I: 119-124).
IEEE DOI BibRef 9000

Betz, J.W., Prince, J.L., Bello, M.G.,
Representation and transformation of uncertainty in an evidence theory framework,
CVPR89(646-652).
IEEE DOI 0403
BibRef

Crowley, J.L., Ramparany, F.,
Mathematical Tools For Representing Uncertainty In Perception,
SRMSF87(293-302). BibRef 8700

Eshera, M.A.,
Hierarchical Inference Scheme For High-Level Image Understanding,
ICPR88(II: 882-884).
IEEE DOI 8811
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Landy, M.S., Hummel, R.A.,
A Brief Survey of Knowledge Aggregation Methods,
ICPR86(248-252). BibRef 8600

Cohen, F.S., Cooper, D.B.,
A Decision Theoretic Approach for 3-D Vision,
CVPR88(964-972).
IEEE DOI BibRef 8800

Huang, C.Z.[Cheng-Zhi], Li, Y.[Yanda], Chang, T.[Tong],
Solving the stiff problem in computer vision by trade-off optimization,
ICPR88(I: 160-162).
IEEE DOI 8811
Linear Inverse problems. BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Fuzzy Sets, Fuzzy Logic .