13.3.5.1 Continuous Relaxation Theory, Constraint Satisfaction

Chapter Contents (Back)
Constraint Satisfaction. Matching, Graphs. Matching, Relaxation. Relaxation, Continuous. See also Improving Edges by Neighborhood Processing, Relaxation, Multi-Scale.

Ullman, S.,
Relaxation and Constrained Optimization by Local Processes,
CGIP(10), No. 2, June 1979, pp. 115-125.
WWW Link. 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 Link. 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 Link. 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 Link. 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 DOI 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 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 Link. 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 Link. BibRef 8103

Lloyd, S.A.,
An Optimization Approach to Relaxation Labeling Algorithms,
IVC(1), No. 2, May 1983, pp. 85-91.
WWW Link. 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.[Tommy], Eklundh, J.O.[Jan-Olof],
Some Properties of Stochastic Labeling Procedures,
CGIP(20), No. 2, October 1982, pp. 158-170.
WWW Link. 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 Link. 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 8811
BibRef

Jamison, T.A., and Schalkoff, R.J.,
Image Labeling: A Neural Network Approach,
IVC(6), No. 4, November 1988, pp. 203-214.
Elsevier DOI 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 DOI 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.E., Han, Y.S., Bilbro, G.L., Whitaker, R.T., Pizer, S.M.,
Image Relaxation: Restoration and Feature-Extraction,
PAMI(17), No. 6, June 1995, pp. 620-624.
IEEE DOI 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 Link. BibRef 9511

Cucka, P., Rosenfeld, A.,
Evidence Based Pattern-Matching Relaxation,
PR(26), No. 9, September 1993, pp. 1417-1427.
WWW Link. BibRef 9309

Pelillo, M.[Marcello],
The Dynamics of Nonlinear Relaxation Labeling Processes,
JMIV(7), No. 4, October 1997, pp. 309-323.
DOI Link 9710
BibRef
Earlier:
Nonlinear relaxation labeling as growth transformation,
ICPR94(B:201-206).
IEEE DOI 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 9208
BibRef

Fu, A.M.N.[Alan M.N.], Yan, H.[Hong],
A New Probabilistic Relaxation Method Based on Probability Space Partition,
PR(30), No. 11, November 1997, pp. 1905-1917.
WWW Link. 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.
DOI Link 9807
BibRef
Earlier:
Probabilistic Relaxation as an Optimiser,
BMVC95(613-622).
PDF File. 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 9509
BibRef

Arathorn, D.W.,
Recognition under transformation using ordering property of superpositions,
EL(37), 2001, 164-166.
DOI Link 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 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 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 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 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 Link. 0808
BibRef
Earlier:
Probabilistic Relaxation Labeling by Fokker-Planck Diffusion on a Graph,
GbRPR07(204-214).
Springer DOI 0706
BibRef
And:
Kernelised Relaxation Labelling using Fokker-Planck Diffusion,
CIAP07(29-34).
IEEE DOI 0709
BibRef
Earlier:
Probabilistic Relaxation using the Heat Equation,
ICPR06(II: 666-669).
IEEE DOI 0609
Data clustering; Feature correspondence matching; Scene labelling; Relaxation labelling; Graph theory; Diffusion process; Fokker-Planck equation BibRef

Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin],
Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications,
IJCV(103), No. 3, July 2013, pp. 306-325.
WWW Link. 1306
BibRef

Domke, J.[Justin],
Learning Graphical Model Parameters with Approximate Marginal Inference,
PAMI(35), No. 10, 2013, pp. 2454-2467.
IEEE DOI 1309
BibRef
Earlier:
Parameter learning with truncated message-passing,
CVPR11(2937-2943).
IEEE DOI 1106
Approximation algorithms. Training conditional random fields. BibRef

Bredies, K.[Kristian], Pock, T.[Thomas], Wirth, B.[Benedikt],
Convex Relaxation of a Class of Vertex Penalizing Functionals,
JMIV(47), No. 3, November 2013, pp. 278-302.
WWW Link.
Springer DOI 1309
Results for image segmentation, image denoising and image inpainting. BibRef

Papadakis, N., Yildizoglu, R., Aujol, J., Caselles, V.,
High-Dimension Multilabel Problems: Convex or Nonconvex Relaxation?,
SIIMS(6), No. 4, 2013, pp. 2603-2639.
DOI Link 1402
BibRef

Sanchez-Diaz, G.[Guillermo], Diaz-Sanchez, G.[German], Mora-Gonzalez, M.[Miguel], Piza-Davila, I.[Ivan], Aguirre-Salado, C.A.[Carlos A.], Huerta-Cuellar, G.[Guillermo], Reyes-Cardenas, O.[Oscar], Cardenas-Tristan, A.[Abraham],
An evolutionary algorithm with acceleration operator to generate a subset of typical testors,
PRL(41), No. 1, 2014, pp. 34-42.
Elsevier DOI 1403
Hill climbers BibRef

Carrasco-Ochoa, J.A.[J. Ariel], Lazo-Cortés, M.S.[Manuel S.], Martínez-Trinidad, J.F.[José Francisco],
An Algorithm for Computing Goldman Fuzzy Reducts,
MCPR17(3-12).
Springer DOI 1706
BibRef

Lazo-Cortés, M.S.[Manuel S.], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco], Sanchez-Diaz, G.[Guillermo],
Computing Constructs by Using Typical Testor Algorithms,
MCPR15(44-53).
Springer DOI 1506
BibRef
Earlier: A1, A3, A2, A4:
Are Reducts and Typical Testors the Same?,
CIARP14(294-301).
Springer DOI 1411
BibRef

Möllenhoff, T.[Thomas], Strekalovskiy, E.[Evgeny], Moeller, M.[Michael], Cremers, D.[Daniel],
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings,
SIIMS(8), No. 2, 2015, pp. 827-857.
DOI Link 1507
BibRef


Shah, S.[Sohil], Yadav, A.K.[Abhay Kumar], Castillo, C.D.[Carlos D.], Jacobs, D.W.[David W.], Studer, C.[Christoph], Goldstein, T.[Tom],
Biconvex Relaxation for Semidefinite Programming in Computer Vision,
ECCV16(VI: 717-735).
Springer DOI 1611
BibRef

Möllenhoff, T.[Thomas], Laude, E.[Emanuel], Moeller, M.[Michael], Lellmann, J.[Jan], Cremers, D.[Daniel],
Sublabel-Accurate Relaxation of Nonconvex Energies,
CVPR16(3948-3956)
IEEE DOI 1612
Award, CVPR, Honorable Mention. BibRef
Earlier: A2, A1, A3, A4, A5:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies,
ECCV16(I: 614-627).
Springer DOI 1611
BibRef

Desmaison, A.[Alban], Bunel, R.[Rudy], Kohli, P.[Pushmeet], Torr, P.H.S.[Philip H. S.], Pawan Kumar, M.,
Efficient Continuous Relaxations for Dense CRF,
ECCV16(II: 818-833).
Springer DOI 1611
BibRef

Do, T.T.[Thanh-Toan], Doan, A.D.[Anh-Dzung], Nguyen, D.T.[Duc-Thanh], Cheung, N.M.[Ngai-Man],
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian,
ECCV16(II: 802-817).
Springer DOI 1611
BibRef

Bai, J.J.[Jun-Jie], Song, Q.[Qi], Veksler, O.[Olga], Wu, X.D.[Xiao-Dong],
Fast dynamic programming for labeling problems with ordering constraints,
CVPR12(1728-1735).
IEEE DOI 1208
BibRef

Alchatzidis, S.[Stavros], Sotiras, A.[Aristeidis], Paragios, N.[Nikos],
Efficient parallel message computation for MAP inference,
ICCV11(1379-1386).
IEEE DOI 1201
BibRef

Osa, A., Zhang, L., Miike, H.,
Error Sources and Error Reduction in Gradient-based Method with Local Optimization,
MVA98(xx-yy). BibRef 9800

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 9608
Colorado State.
WWW Link. BibRef

Poole, I.,
Optimal probabilistic relaxation labeling,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Bozma, H.I., and Duncan, J.S.,
Admissibility of Constraint Functions in Relaxation Labeling,
ICCV88(328-332).
IEEE DOI 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 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 .


Last update:Jun 28, 2017 at 11:44:43