Ullman, S.,
Relaxation and Constrained Optimization by Local Processes,
CGIP(10), No. 2, June 1979, pp. 115-125.
WWW Version.
BibRef
7906
Nagin, P.A.,
Hanson, A.R.,
Riseman, E.M.,
Variations in Relaxation Labeling Techniques,
CGIP(17), No. 1, September 1981, pp. 33-51.
WWW Version.
BibRef
8109
Richards, J.A.,
Landgrebe, D.A.,
Swain, P.H.,
On the Accuracy of Pixel Relaxation,
SMC(11), 1981, pp. 303-309.
BibRef
8100
Glazer, F.,
Multilevel Relaxation in Low-Level Computer Vision,
MIPA84(312-330).
BibRef
8400
Kittler, J.V., and
Illingworth, J.,
Relaxation Labelling Algorithms: A Review,
IVC(3), No. 4, November 1985, pp. 206-216.
WWW Version.
Survey, Relaxation.
Relaxation, Survey.
BibRef
8511
Davis, L.S., and
Rosenfeld, A.,
Cooperating Processes for Low-Level Vision: A Survey,
AI(17), No. 1-3, August 1981, pp. 245-263.
WWW Version.
Survey, Relaxation.
Relaxation, Survey.
BibRef
8108
Haralick, R.M.,
Davis, L.S.,
Rosenfeld, A.[Azriel], and
Milgram, D.L.[David L.],
Reduction Operations for Constraint Satisfaction,
IS(14), No. 3, April, 1978, pp. 199-219.
BibRef
7804
Zhuang, X.,
Haralick, R.M., and
Joo, H.,
A Simplex-Like Algorithm for the Relaxation Labeling Process,
PAMI(11), No. 12, December 1989, pp. 1316-1321.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
8912
Earlier:
ICPR86(190-194).
A new 1 iteration procedure. It is compared to the original RHZ
relaxation and does perform better (but then everything does).
BibRef
Haralick, R.M.[Robert M.],
Decision Making in Context,
PAMI(5), No. 4, July 1983, pp. 417-428.
Bayes Networks.
BibRef
8307
Earlier:
Contextual decision making with degrees of belief,
ICPR92(II:105-111).
IEEE DOI Link
9208
Discusses relaxation and how it gets around the problems of the usual
Bayesian decision theoretic models.
BibRef
Haralick, R.M.[Robert M.],
An Interpretation for Probabilistic Relaxation,
CVGIP(22), No. 3, June 1983, pp. 388-395.
WWW Version. Each iteration is a new computation of the conditional probability for the new
context. Therefore iterations need to continue only until all the context has
been considered. How to determine this is still an open question.
BibRef
8306
Krishnamurthy, E.V.,
Narayanan, K.A.,
Relaxation: Application to the Matrix Reconstruction Problem,
CGIP(15), No. 3, March 1981, pp. 288-295.
WWW Version.
BibRef
8103
Lloyd, S.A.,
An Optimization Approach to Relaxation Labeling Algorithms,
IVC(1), No. 2, May 1983, pp. 85-91.
WWW Version.
BibRef
8305
Kalayeh, H.M., and
Landgrebe, D.A.,
Adaptive Relaxation Labeling,
PAMI(6), No. 3, May, 1984, pp. 369-372.
The problems with the constant compatibility coefficients. The fix is
to estimate the compatibility coefficients based on small neighborhoods.
BibRef
8405
Fekete, G.,
Eklundh, J.O., and
Rosenfeld, A.,
Relaxation: Evaluation and Applications,
PAMI(3), No. 4, July 1981, pp. 459-469.
BibRef
8107
Eklundh, J.O., and
Rosenfeld, A.,
Some Relaxation Experiments Using Triples of Pixels,
SMC(10), 1980, pp. 150-153.
BibRef
8000
Eklundh, J.O., and
Rosenfeld, A.,
Convergence Properties of Relaxation,
UMD-TR-701, October 1978.
BibRef
7810
Elfving, T., and
Eklundh, J.O.,
Some Properties of Stochastic Labeling Procedures,
CGIP(20), No. 2, October 1982, pp. 158-170.
WWW Version. A particular model of relaxation processes is formulated and used to
analyze the basic methods. Some mention of optimizing methods.
BibRef
8210
Kuschel, S.A., and
Page, C.V.,
Augmented Relaxation Labeling and Dynamic Relaxation Labeling,
PAMI(4), No. 6, November 1982, pp. 676-683.
BibRef
8211
Earlier:
PRIP81(441-448).
Augmentation to give nonhomogeneous neighborhood. The value of a
point is broadcast to a specific neighborhood (depending on its likely
assignment) rather than to all neighbors.
BibRef
Wong, A.K.C.[Andrew K. C.],
Chiu, D.K.Y.[David K. Y.],
An event-covering method for effective probabilistic inference,
PR(20), No. 2, 1987, pp. 245-255.
WWW Version.
0309
BibRef
Earlier:
A Probabilistic Inference System,
ICPR84(303-306).
BibRef
Chan, K.C.C.,
Wong, A.K.C.,
PIS: a probabilistic inference system,
ICPR88(I: 360-364).
IEEE DOI Link
8811
BibRef
Jamison, J.S., and
Schalkoff, R.J.,
Image Labeling: A Neural Network Approach,
IVC(6), No. 4, November 1988, pp. 203-214.
WWW Version.
BibRef
8811
Duncan, J.S., and
Frei, W.,
Relaxation Labeling Using Continuous Label Sets,
PRL(9), No. 1, January 1989, pp. 27-37.
BibRef
8901
Soo, V.W.,
Huang, K.,
On Evidential Relaxation Labeling:
A Scheme Toward Knowledge-Based Vision,
JISE(9), No. 2, 1993, pp. 153-175.
BibRef
9300
Fogel, D.B.,
An Introduction to Simulated Evolutionary Optimization,
TNN(5), No. 1, 1994, pp. 3-14.
Problems with hill-climbing in local optimization.
BibRef
9400
Sastry, P.S.,
Thathachar, M.A.L.,
Analysis of Stochastic Automata Algorithm for Relaxation Labelling,
PAMI(16), No. 5, May 1994, pp. 538-543.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9405
Qi, X.F., and
Palmieri, F.,
Theoretical Analysis of Evolutionary Algorithms with an Infinite Population
in Continuous Space: Basic Properties of Selection and Mutation,
TNN(5), 1994, pp. 102-119.
BibRef
9400
And:
Theoretical Analysis of Evolutionary Algorithms with an Infinite Population
in Continuous Space: Analysis of the Diversification Role of Crossover,
TNN(5), 1994, pp. 120-129.
Genetic Algorithms.
BibRef
Snyder, W.,
Han, Y.S.,
Bilbro, G.L.,
Whitaker, R.T.,
Pizer, S.,
Image Relaxation: Restoration and Feature-Extraction,
PAMI(17), No. 6, June 1995, pp. 620-624.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Image Restoration. Equivalence with Graduated Nonconvexity, Variable Conductance Diffusion,
Anisotropic Diffusion and Biased Anisotropic Diffusion, Mean Field
Annealing and Image Relaxation.
BibRef
9506
Pelillo, M.,
Abbattista, F.,
Maffione, A.,
An Evolutionary Approach to Training Relaxation Labeling Processes,
PRL(16), No. 10, October 1995, pp. 1069-1078.
BibRef
9510
Chen, Q.,
Luh, J.Y.S.,
Relaxation Labeling Algorithm for Information Integration and its
Convergence,
PR(28), No. 11, November 1995, pp. 1705-1722.
WWW Version.
BibRef
9511
Cucka, P.,
Rosenfeld, A.,
Evidence Based Pattern-Matching Relaxation,
PR(26), No. 9, September 1993, pp. 1417-1427.
WWW Version.
BibRef
9309
Pelillo, M.[Marcello],
The Dynamics of Nonlinear Relaxation Labeling Processes,
JMIV(7), No. 4, October 1997, pp. 309-323.
WWW Version.
9710
BibRef
Earlier:
Nonlinear relaxation labeling as growth transformation,
ICPR94(B:201-206).
IEEE DOI Link
9410
BibRef
Pelillo, M.[Marcello],
Refice, M.,
An optimization algorithm for determining the compatibility
coefficients of relaxation labeling processes,
ICPR92(II:145-148).
IEEE DOI Link
9208
BibRef
Fu, A.M.N.,
Yan, H.,
A New Probabilistic Relaxation Method Based on
Probability Space Partition,
PR(30), No. 11, November 1997, pp. 1905-1917.
WWW Version.
9801
BibRef
Stoddart, A.J.,
Petrou, M.[Maria],
Kittler, J.V.,
On the Foundations of Probabilistic Relaxation with Product Support,
JMIV(9), No. 1, July 1998, pp. 29-48.
WWW Version.
9807
BibRef
Earlier:
Probabilistic Relaxation as an Optimiser,
BMVC95(613-622).
PDF Version.
9509
BibRef
Stoddart, A.J.,
Petrou, M.,
Kittler, J.V.,
A New Algorithm for Probabilistic Relaxation Based on the
Baum Eagon Theorem,
CAIP95(674-679).
Springer DOI Link
9509
BibRef
Arathorn, D.W.,
Recognition under transformation using ordering property
of superpositions,
EL(37), 2001, 164-166.
WWW Version.
Map-Seeking Circuit Algorithm
BibRef
0100
Gedeon, T.[Tomáš],
Arathorn, D.W.[David W.],
Convergence of Map Seeking Circuits,
JMIV(29), No. 2-3, November 2007, pp. 235-248.
Springer DOI Link
0712
BibRef
Jacobson, M.W.,
Fessler, J.A.,
An Expanded Theoretical Treatment of Iteration-Dependent
Majorize-Minimize Algorithms,
IP(16), No. 10, October 2007, pp. 2411-2422.
IEEE DOI Link
0711
Iterative process (relaxation), first majorize one, then minimize another.
BibRef
Chen, X.,
Li, Y.,
A Modified PSO Structure Resulting in High Exploration Ability With
Convergence Guaranteed,
SMC-B(37), No. 5, October 2007, pp. 1271-1289.
IEEE DOI Link
0711
Particle swarm optimization.
Simulate swarm of insects.
BibRef
Harker, S.R.,
Vogel, C.R.,
Gedeon, T.,
Analysis of Constrained Optimization Variants of the Map-Seeking
Circuit Algorithm,
JMIV(29), No. 1, Septmeber 2007, pp. 49-62.
Springer DOI Link
0709
Efficiently solve the combinatorial problem of correspondence maximization.
See also Recognition under transformation using ordering property of superpositions.
BibRef
Wang, H.F.[Hong-Fang],
Hancock, E.R.[Edwin R.],
Probabilistic relaxation labelling using the Fokker-Planck equation,
PR(41), No. 11, November 2008, pp. 3393-3411.
WWW Version.
0808
BibRef
Earlier:
Probabilistic Relaxation Labeling by Fokker-Planck Diffusion on a Graph,
GbRPR07(204-214).
Springer DOI Link
0706
BibRef
And:
Kernelised Relaxation Labelling using Fokker-Planck Diffusion,
CIAP07(29-34).
IEEE DOI Link
0709
BibRef
Earlier:
Probabilistic Relaxation using the Heat Equation,
ICPR06(II: 666-669).
WWW Version.
0609
Data clustering; Feature correspondence matching; Scene labelling;
Relaxation labelling; Graph theory; Diffusion process; Fokker-Planck equation
BibRef
Petrou, M.,
Mirmehdi, M., and
Coors, M.,
Multilevel Probabilistic Relaxation,
BMVC97(60-69).
HTML Version. Segmentation technique.
BibRef
9700
Draper, B.A.,
Modelling Object Recognition as a Markov Decision Process,
ICPR96(IV: 95-99).
IEEE DOI Link
9608
Colorado State.
WWW Version.
BibRef
Poole, I.,
Optimal probabilistic relaxation labeling,
BMVC90(xx-yy).
PDF Version.
9009
BibRef
Bozma, H.I., and
Duncan, J.S.,
Admissibility of Constraint Functions in Relaxation Labeling,
ICCV88(328-332).
IEEE Abstract. IEEE Top Reference. Conditions on the constraint functions in a relaxation process
that is solving an optimization problem.
BibRef
8800
Zhang, D.,
Liu, J.,
Wan, F.,
Multiresolution Relaxation: Experiments and Evaluations,
ICPR88(II: 712-714).
IEEE DOI Link
IEEE Top Reference.
BibRef
8800
Thompson, W.B.,
Mutch, K.M.,
Kearney, J.K.,
Madarasz, R.L.,
Relaxation Labeling Using Staged Updating,
PRIP81(449-451).
BibRef
8100
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Boltzmann Machine, Simulated Annealing, and Related Topics .