13.3.8.1 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.
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. 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 Link 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. BibRef 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).
PDF Version. 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).
Springer DOI Link BibRef

Kittler, J.V., Christmas, W.J., and Petrou, M.,
Probabilistic Relaxation for Matching Problems in Computer Vision,
ICCV93(666-673).
IEEE DOI Link 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.
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. 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. IEEE Top Reference. 0407
BibRef

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

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
BibRef

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 of acute abdominal pain,
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).
IEEE DOI Link 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.
IEEE DOI Link 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). 0208
Time 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.
IEEE DOI Link 0806
BibRef
Earlier:
Matching with PROSAC: Progressive Sample Consensus,
CVPR05(I: 220-226).
IEEE DOI Link 0507
BibRef
And: A2, A1:
Randomized RANSAC with Sequential Probability Ratio Test,
ICCV05(II: 1727-1732).
IEEE DOI Link 0510
BibRef

Chum, O.[Ondrej], Matas, J.[Jiri],
Geometric Hashing with Local Affine Frames,
CVPR06(I: 879-884).
IEEE DOI Link 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.
IEEE DOI Link 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

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 Link 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 Link 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 Link 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. 0711
Evidence 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.
IEEE DOI Link 0810
Reformulate GC as L1 min for energy minimization problems. 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.
WWW Version. 0810
BibRef
Earlier:
Solving Large Scale Binary Quadratic Problems: Spectral Methods vs. Semidefinite Programming,
CVPR07(1-8).
IEEE DOI Link 0706
BibRef
And:
Efficient Optimization for L-inf-problems using Pseudoconvexity,
ICCV07(1-8).
IEEE DOI Link 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.[Anders],
Solving quadratically constrained geometrical problems using lagrangian duality,
ICPR08(1-5).
IEEE DOI Link 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 Link 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 Link 0902
BibRef
Earlier:
Global Optimization through Searching Rotation Space and Optimal Estimation of the Essential Matrix,
ICCV07(1-8).
IEEE DOI Link 0710
Branch-and-bound search over rotations. BibRef

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

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

Chandraker, M.[Manmohan], Kriegman, D.J.[David J.],
Globally optimal bilinear programming for computer vision applications,
CVPR08(1-8).
IEEE DOI Link 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 Link 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 Link 0806
Search for maximum in scale space. 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).
Springer DOI Link 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).
IEEE DOI Link 0710
BibRef

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

Gangaputra, S.[Sachin], Geman, D.[Donald],
Self-normalized linear tests,
CVPR04(II: 616-622).
IEEE Abstract. IEEE Top Reference. 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 Link 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.[Mark J.L.], Fisher, R.B.[Robert B.], Hallam, J.[John],
Computing with Uncertainty: Intervals versus Probabilities,
BMVC91(351-354).
PDF Version. 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 Version. 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 Link 9509
BibRef

Besserer, B., Estable, S., Ulmer, B.,
Multiple knowledge sources and evidential reasoning for shape recognition,
ICCV93(624-631).
IEEE DOI Link 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 Link 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,
SRMSF87(293-302). BibRef 8700

Eshera, M.A.,
Hierarchical Inference Scheme For High-Level Image Understanding,
ICPR88(II: 882-884).
IEEE DOI Link 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.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 Link 8811
Linear Inverse problems. BibRef

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