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.
WWW Version.
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.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
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.
WWW Version.
BibRef
8808
Barnett, J.A.,
Calculating Dempster-Shafer Plausibility,
PAMI(13), No. 6, June 1991, pp. 599-602.
IEEE Abstract. IEEE Top Reference.
WWW Version. 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.
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8208
Sher, D.B.,
Hull, J.J.,
Quantifying the unimportance of prior probabilities in a computer
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ICPR90(I: 662-664).
WWW Version.
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.
WWW Version.
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.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
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.
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9600
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.
WWW Version.
9609
BibRef
Earlier:
Modelling Compatibility Coefficient Distributions for
Probabilistic Feature-Labelling Schemes,
BMVC95(603-612).
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.
WWW Version.
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).
WWW Version.
BibRef
Kittler, J.V.,
Christmas, W.J., and
Petrou, M.,
Probabilistic Relaxation for Matching Problems in Computer Vision,
ICCV93(666-673).
WWW Version. 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
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PRL(26), No. 3, February 2005, pp. 381-393.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
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. IEEE Top Reference.
0403How reliable is a sensor in a data fusion application.
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Delmotte, F.,
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Target Identification Based on the Transferable Belief Model
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IEEE Abstract. IEEE Top Reference.
0407
BibRef
Abramson, B.,
Expected-outcome: a general model of static evaluation,
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IEEE Abstract. IEEE Top Reference.
WWW Version.
0401
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Bhatnagar, R.,
Kanal, L.N.,
Structural and probabilistic knowledge for abductive reasoning,
PAMI(15), No. 3, March 1993, pp. 233-245.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0401
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Fertig, K.W.,
Breese, J.S.,
Probability intervals over influence diagrams,
PAMI(15), No. 3, March 1993, pp. 280-286.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0401
BibRef
Wellman, M.P.,
Henrion, M.,
Explaining 'explaining away',
PAMI(15), No. 3, March 1993, pp. 287-292.
IEEE Abstract. IEEE Top Reference.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
0401
BibRef
Provan, G.M.,
Clarke, J.R.,
Dynamic network construction and updating techniques for the diagnosis
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PAMI(15), No. 3, March 1993, pp. 299-307.
IEEE Abstract. IEEE Top Reference.
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
0401
BibRef
Sucar, L.E.,
Gillies, D.F.,
Probabilistic Reasoning in High-Level Vision,
IVC(12), No. 1, January-February 1994, pp. 42-60.
WWW Version.
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.
WWW Version.
BibRef
9402
Earlier:
Some new techniques for evidence-based object recognition: EB-ORS1,
ICPR92(II:450-454).
WWW Version.
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 Abstract. IEEE Top Reference.
WWW Version.
BibRef
8906
Earlier:
Construction and Interpretations of Formulas for Combining Evidence,
ICPR86(1226-1229).
BibRef
Mogre, A.,
McLaren, R.,
Keller, J.,
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.
WWW Version.
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.
WWW Version.
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.
WWW Version.
0401
BibRef
Cuzzolin, F.,
Geometry of Dempster's Rule of Combination,
SMC-B(34), No. 2, April 2004, pp. 961-977.
IEEE Abstract. IEEE Top Reference.
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.
WWW Version.
0707
BibRef
Matas, J.[Jirí],
Chum, O.[Ondrej],
Randomized RANSAC with Td,d test,
IVC(22), No. 10, 1 September 2004, pp. 837-842.
WWW Version.
0409
BibRef
Earlier:
Randomized RANSAC with T(d,d) test,
BMVC02(Computer Vision Tools).
0208Time savings from evaluating only a part of the points.
BibRef
Chum, O.[Ondrej],
Matas, J.[Jirí],
Optimal Randomized RANSAC,
PAMI(30), No. 8, August 2008, pp. 1472-1482.
WWW Version.
0806
BibRef
Earlier:
Matching with PROSAC: Progressive Sample Consensus,
CVPR05(I: 220-226).
WWW Version.
0507
BibRef
And: A2, A1:
Randomized RANSAC with Sequential Probability Ratio Test,
ICCV05(II: 1727-1732).
WWW Version.
0510
Chum, O.[Ondrej],
Matas, J.[Jiri],
BibRef
Geometric Hashing with Local Affine Frames,
CVPR06(I: 879-884).
WWW Version.
0606
BibRef
Chum, O.[Ondrej],
Matas, J.[Jiri],
Kittler, J.V.[Josef V.],
Locally Optimized RANSAC,
DAGM03(236-243).
HTML Version.
0310
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.
WWW Version.
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.
WWW Version.
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.
WWW Version.
0602
BibRef
Kolmogorov, V.,
Convergent Tree-Reweighted Message Passing for Energy Minimization,
PAMI(28), No. 10, October 2006, pp. 1568-1583.
WWW Version.
0609 See also MAP Estimation via Agreement on (Hyper)Trees: Message-Passing and Linear-Programming Approaches.
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.
WWW Version.
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.
WWW Version.
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.
WWW Version.
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.
WWW Version.
0711Evidence theory; Belief functions; Grey relational analysis;
Conflict reassignment; Target recognition; Reliability evaluation
BibRef
Bhusnurmath, A.[Arvind],
Taylor, C.J.[Camillo J.],
Graph Cuts via L_1 Norm Minimization,
PAMI(30), No. 10, October 2008, pp. 1866-1871.
WWW Version.
0810Reformulate GC as L1 min for energy minimization problems.
BibRef
Agarwal, S.[Sameer],
Snavely, N.[Noah],
Seitz, S.M.[Steven M.],
Fast algorithms for L-inf problems in multiview geometry,
CVPR08(1-8).
WWW Version.
0806Optimization problems.
See also Efficient Optimization for L-inf-problems using Pseudoconvexity.
BibRef
Olsson, C.[Carl],
Eriksson, A.P.[Anders P.],
Kahl, F.[Fredrik],
Efficient Optimization for L-inf-problems using Pseudoconvexity,
ICCV07(1-8).
WWW Version.
0710
BibRef
Eriksson, A.P.[Anders P.],
Olsson, C.[Carl],
Kahl, F.[Fredrik],
Efficiently Solving the Fractional Trust Region Problem,
ACCV07(II: 796-805).
WWW Version.
0711
BibRef
Olsson, C.[Carl],
Eriksson, A.P.[Anders P.],
Kahl, F.[Fredrik],
Solving Large Scale Binary Quadratic Problems:
Spectral Methods vs. Semidefinite Programming,
CVPR07(1-8).
WWW Version.
0706For relaxation implementations.
BibRef
Leordeanu, M.[Marius],
Hebert, M.[Martial],
Smoothing-based Optimization,
CVPR08(1-8).
WWW Version.
0806Search for maximum in scale space.
BibRef
Hartley, R.I.[Richard I.],
Kahl, F.[Fredrik],
Global Optimization through Searching Rotation Space and Optimal
Estimation of the Essential Matrix,
ICCV07(1-8).
WWW Version.
0710
BibRef
Nwogu, I.[Ifeoma],
Corso, J.J.[Jason J.],
(BP)2: Beyond pairwise Belief Propagation labeling by approximating
Kikuchi free energies,
CVPR08(1-8).
WWW Version.
0806
BibRef
Petersen, K.[Kersten],
Fehr, J.[Janis],
Burkhardt, H.[Hans],
Fast Generalized Belief Propagation for MAP Estimation on 2D and 3D
Grid-Like Markov Random Fields,
DAGM08(xx-yy).
WWW Version.
0806
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).
WWW Version.
0710
BibRef
Kotb, Y.T.,
Beauchemin, S.S.,
Barron, J.L.,
Petri Net-Based Cooperation In Multi-Agent Systems,
CRV07(123-130).
WWW Version.
0705
BibRef
Gangaputra, S.[Sachin],
Geman, D.[Donald],
Self-normalized linear tests,
CVPR04(II: 616-622).
IEEE Abstract. IEEE Top Reference.
0408Find 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).
WWW Version.
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 Abstract. IEEE Top Reference.
0307
BibRef
Earlier:
Nonparametric Belief Propagation and Facial Appearance Estimation,
MIT AIMAIM-2002-020, December 2002.
WWW Version. 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
BibRef
Orr, M.J.L.,
Fisher, R.B.,
Hallam, J.,
Computing with Uncertainty: Intervals versus Probability,
BMVC91(351-354).
BibRef
9100
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).
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).
WWW Version.
9509
BibRef
Besserer, B.,
Estable, S.,
Ulmer, B.,
Multiple knowledge sources and evidential reasoning for shape
recognition,
ICCV93(624-631).
WWW Version.
0403Uncertainty 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).
WWW Version.
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 Abstract. IEEE Top Reference.
0403
BibRef
Crowley, J.L.,
Ramparany, F.,
Mathematical Tools For Representing Uncertainty In Perception,
SPMSF87(293-302).
BibRef
8700
Eshera, M.A.,
Hierarchical Inference Scheme For High-Level Image Understanding,
ICPR88(II: 882-884).
WWW Version.
8811
BibRef
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 Abstract. IEEE Top Reference.
BibRef
8800
Huang, C.[Chengzhi],
Li, Y.[Yanda],
Chang, T.[Tong],
Solving the stiff problem in computer vision by trade-off optimization,
ICPR88(I: 160-162).
WWW Version.
8811Linear Inverse problems.
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Fuzzy Sets, Fuzzy Logic .