Barnard, S.T., and
Thompson, W.B.,
Disparity Analysis of Images,
PAMI(2), No. 4, July 1980, pp. 333-340.
BibRef
8007
Earlier:
TR-79-1, CSD,
Univ. of MinnesotaJanuary 1979.
Relaxation, Results.
Matching, Points. Matching for motion. This program finds corresponding pairs of points
in a motion analysis system using the similarity of motion with
neighboring points. Feature points (such as corners) in both views
are used rather than the single view used in Moravec, and a
relaxation procedure finds the final global match between the two sets
of feature points. The initial assignments of possible matches for
the set of feature points is simply all the features with a similar
(nearby) position in the second image. Thus, small motions are
assumed. An iterative (relaxation based) procedure uses the
disparities of the nearby points to eliminate the unlikely assignments
from the set of possible assignments. These include points with
disparities different from the others in the neighborhood. The
formulation of the algorithm is very simple and thus it works for any
kind of disparity (such as from observer motion, multiple object
motions, or stereo) and it does not require any detailed camera
models. This provides a basic matching method to find disparity for a
moderate number of points (the feature points) that are generally
consistent with the other nearby points (i.e. smooth surfaces), but
allowing for edges or changes in the disparity field.
See also Lower-level Estimates and Interpretation of Visual Motion.
BibRef
Barnard, S.T.,
The Image Correspondence Problem,
Ph.D.Thesis (CS), U Minn, 1979.
The thesis version of his work.
BibRef
7900
Kitchen, L.,
Relaxation Applied to Matching Quantitative Relational Structures,
SMC(10), February 1980, pp. 96-101.
Fuzzy Logic. Introduction of a new operator defined in terms of fuzzy logic with
some examples on synthetic structures. Experiments with the
operator on more general problems indicate that there may be
problems which are not indicated by the synthetic problems.
BibRef
8002
Yamamoto, H.,
Some Experiments on LANDSAT Pixel Classification
Using Relaxation Operators,
CGIP(13), No. 1, May 1980, pp. 31-45.
WWW Version.
BibRef
8005
Kirby, R.L.,
A Product Rule Relaxation Method,
CGIP(13), No. 2, June 1980, pp. 158-189.
WWW Version.
BibRef
8006
Hwang, J.J., and
Hall, E.L.,
Matching of Featured Objects Using
Relational Tables from Stereo Images,
CGIP(20), No. 1, September 1982, pp. 22-42.
WWW Version.
Matching, Regions. Features include regions, lines and vertices. The example is a
complex block-like UT. The structure is simply adjacencies. The
arrays are used to simplify the search for the matching subset.
They use precise knowledge of the camera locations to get search
lines in the second image.
BibRef
8209
Hwang, J.J., and
Hall, E.L.,
Scene Representation Using the Adjacency Matrix and
Sampled Shapes of Regions,
PRIP78(250-261).
BibRef
7800
Faugeras, O.D., and
Price, K.E.,
Semantic Description of Aerial Images Using Stochastic Labeling,
PAMI(3), No. 6, November 1981, pp. 633-642.
BibRef
8111
USC Computer Vision
BibRef
And:
ICPR80(352-357).
BibRef
And:
DARPA80(89-94).
Matching, Regions.
Relaxation, Results.
The use of an optimization based relaxation method with structural
descriptions. This work uses a relaxation approach very similar to
that of (
See also Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach. ) for finding corresponding regions in two
images of the same scene and finding regions in the image
corresponding to elements in a model of the scene.
The relaxation matching procedure has two major steps: finding initial
potential matches and computing the updated match rating based on the
matches for the neighboring regions. These steps are combined by:
(1) Compute the match rating for each region in the model with all regions
in the image. Order these and keep only the best (15) matches.
(2)Compute the compatibility for each of these possible matches with the
current most likely match for all the neighboring (related in the
network) regions. (3) Update the match ratings so that compatible matches
improve and incompatible ones decrease. (4) If some match is very likely,
make the assignment permanent, and continue with the initialization
step. Otherwise continue with the compatibility computation step.
This procedure works by finding the most obvious match (e.g. largest
regions, and all other features match) and building around this one by
making assignments to regions related to the obvious match. This
matching system makes few assumptions about the types of scenes,
though assumptions can be used to improve the efficiency of the match,
and is applicable to a variety of tasks.
See also Symbolic Image Registration and Change Detection.
BibRef
Price, K.E.,
Hierarchial Matching Using Relaxation,
CVGIP(34), No. 1, April 1986, pp. 66-75.
WWW Version.
BibRef
8604
USC Computer VisionDiscussion of the use of group level descriptions to aid relaxation.
BibRef
Price, K.E.,
Relaxation Matching Techniques - A Comparison,
PAMI(7), No. 5, September 1985, pp. 617-623.
BibRef
8509
USC Computer Vision
BibRef
And:
ICPR84(987-989).
Relaxation, Evaluation.
Comparison of several relaxation methods, for accuracy and time.
BibRef
Price, K.E.,
Symbolic Matching of Images and Scene Models,
DARPA82(299-308).
BibRef
8200
USC Computer Vision
BibRef
And:
CVWS82(105-112).
Several discussions on relaxation techniques in one paper. The
See also Relaxation Matching Techniques - A Comparison. and
See also Hierarchial Matching Using Relaxation. supersede this one.
BibRef
Price, K.E.,
Relaxation Matching Applied to Aerial Images,
DARPA81(22-25).
BibRef
8100
USC Computer VisionDiscussion of more recent results. Not much else.
BibRef
Price, K.E.,
Symbolic Matching and Analysis with Substantial Changes in Orientation,
DARPA78(93-99).
BibRef
7800
USC Computer Vision
BibRef
And:
PRAI-78(19-21).
BibRef
Hummel, R.A.[Robert A.],
A Design Method for Relaxation Labeling Applications,
AAAI-83(168-171).
BibRef
8300
Earlier:
NYUCS Dept., TR 68, March 1983.
A discussion of how to set up a relaxation labeling system.
BibRef
Pelkowitz, L.,
A Continuous Relaxation Labeling Algorithm for Markov Random Fields,
SMC(20), 1990, pp. 709-715.
BibRef
9000
Li, S.Z.,
Matching: Invariant to Translations, Rotations, and Scale Changes,
PR(25), No. 6, June 1992, pp. 583-594.
WWW Version.
BibRef
9206
Ogawa, H.,
A Fuzzy Relaxation Technique For Partial Shape-Matching,
PRL(15), No. 4, April 1994, pp. 349-355.
BibRef
9404
Qin, C.,
Luh, J.Y.S.,
Ambiguity Reduction by Relaxation Labeling,
PR(27), No. 1, January 1994, pp. 165-180.
WWW Version.
BibRef
9401
Ranganath, H.S., and
Chipman, L.J.,
Fuzzy Relaxation Approach for Inexact Scene Matching,
IVC(10), No. 9, November 1992, pp. 631-640.
WWW Version.
Matching, Regions.
BibRef
9211
Cooper, P.R.[Paul R.],
Swain, M.J.[Michael J.],
Arc Consistency: Parallelism and Domain Dependence,
AI(58), No. 1-3, 1992, pp. 207-23.5
WWW Version.
BibRef
9200
Cooper, P.R.[Paul R.],
Swain, M.J.[Michael J.],
Domain Dependence in Parallel Constraint Satisfaction,
IJCAI89(54-59).
BibRef
8900
Swain, M.J.[Michael J.],
Cooper, P.R.[Paul R.],
Parallel Hardware for Constraint Satisfaction,
AAAI-88(682-686).
BibRef
8800
Gold, S.[Steven],
Rangarajan, A.[Anand],
A Graduated Assignment Algorithm for Graph Matching,
PAMI(18), No. 4, April 1996, pp. 377-388.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9604
YaleDCS/RR-1062, January 1995.
BibRef
And:
Graph Matching by Graduated Assignment,
CVPR96(239-244).
IEEE Abstract. IEEE Top Reference.
WWW Version. Matching O(lm).
Similar to relaxation (annealing) approach. (But not quite).
Uses hand labeled features in the image for matching (multiple features
on an object). They note that relaxation labeling does poorly on
pure subgraph isomorphism (no attributed nodes), and does poorly
when noise is high for attributed graph matching. (Though the comparison
is with the most basic relaxation methodology.)
9605
BibRef
Gold, S.[Steven],
Matching and Learning Structural and Spatial Representation
with Neural Networks,
Ph.D.Thesis, Yale, 1995.
BibRef
9500
Gold, S.[Steven],
Rangarajan, A.[Anand], and
Mjolsness, E.,
Learning with Preknowledge:
Clustering with Point and Graph Matching Distance Measures,
NeurComp(8), 1966, pp. 787-804.
BibRef
6600
Sitaraman, R.,
Rosenfeld, A.,
Probabilistic Analysis of Two Stage Matching,
PR(22), No. 3, 1989, pp. 331-343.
WWW Version.
BibRef
8900
Finch, A.M.[Andrew M.],
Wilson, R.C.,
Hancock, E.R.[Edwin R.],
Matching Delaunay Graphs,
PR(30), No. 1, January 1997, pp. 123-140.
WWW Version.
9702
BibRef
Earlier: A1, A3 only:
CIAP95(56-61).
Springer DOI Reference
9509
BibRef
Finch, A.M.[Andrew M.],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Matching delaunay triangulations by probabilistic relaxation,
CAIP95(350-358).
Springer DOI Reference
9509
BibRef
Finch, A.M.,
Hancock, E.R.,
Matching Deformed Delaunay Triangulations,
SCV95(31-36).
IEEE Top Reference. Univ. of York.
Relaxation applied to matching graphs composed of triangles.
BibRef
9500
Bhattacharya, P.,
Some Remarks on Fuzzy Graphs,
PRL(6), 1987, pp. 297-302.
BibRef
8700
Pelillo, M.,
Fanelli, A.M.,
Autoassociative Learning in Relaxation Labeling Networks,
PRL(18), No. 1, January 1997, pp. 3-12.
9704
BibRef
Earlier:
ICPR96(IV: 105-110).
IEEE DOI Reference
9608(Univ. Ca Foscari Venezia, I)
BibRef
Do, K.H.,
Kim, Y.S.,
Uam, T.U.,
Ha, Y.H.,
Iterative Relaxational Stereo Matching Based on
Adaptive Support Between Disparities,
PR(31), No. 8, August 1998, pp. 1049-1059.
WWW Version.
9807
Stereo, Matching.
BibRef
Skomorowski, M.[Marek],
Use of random graph parsing for scene labelling by probabilistic
relaxation,
PRL(20), No. 8, August 1999, pp. 949-956.
BibRef
9908
Torsello, A.[Andrea],
Pelillo, M.[Marcello],
Continuous-time relaxation labeling processes,
PR(33), No. 11, November 2000, pp. 1897-1908.
WWW Version.
0011
BibRef
Medasani, S.,
Krishnapuram, R.,
Choi, Y.S.,
Graph Matching by Relaxation of Fuzzy Assignments,
Fuzzy(9), No. 1, 2001, pp. 173-182.
BibRef
0100
Bengoetxea, E.[Endika],
Larraņaga, P.[Pedro],
Bloch, I.[Isabelle],
Perchant, A.[Aymeric],
Boeres, C.[Claudia],
Inexact graph matching by means of estimation of distribution
algorithms,
PR(35), No. 12, December 2002, pp. 2867-2880.
WWW Version.
0209
BibRef
Earlier: A1, A2, A3, A4, Only:
Estimation of Distribution Algorithms: A New Evolutionary Computation
Approach for Graph Matching Problems,
EMMCVPR02(454 ff.).
HTML Version.
0205
BibRef
Perchant, A.,
Bloch, I.,
Graph Fuzzy Homomorphism Interpreted as Fuzzy Association Graphs,
ICPR00(Vol II: 1042-1045).
IEEE DOI Reference
HTML Version.
0009
BibRef
Aldea, E.[Emanuel],
Fouquier, G.[Geoffroy],
Atif, J.[Jamal],
Bloch, I.[Isabelle],
Kernel Fusion for Image Classification Using Fuzzy Structural
Information,
ISVC07(II: 307-317).
Springer DOI Reference
0711
BibRef
Aldea, E.[Emanuel],
Atif, J.[Jamal],
Bloch, I.[Isabelle],
Image Classification Using Marginalized Kernels for Graphs,
GbRPR07(103-113).
Springer DOI Reference
0706
BibRef
Fouquier, G.[Geoffroy],
Atif, J.[Jamal],
Bloch, I.[Isabelle],
Local Reasoning in Fuzzy Attribute Graphs for Optimizing Sequential
Segmentation,
GbRPR07(138-147).
Springer DOI Reference
0706
BibRef
Cesar, Jr., R.M.[Roberto M.],
Bengoetxea, E.[Endika],
Bloch, I.[Isabelle],
Larraņaga, P.[Pedro],
Inexact graph matching for model-based recognition:
Evaluation and comparison of optimization algorithms,
PR(38), No. 11, November 2005, pp. 2099-2113.
WWW Version.
0509
BibRef
Earlier: A1, A2, A3, Only:
Inexact graph matching using stochastic optimization techniques for
facial feature recognition,
ICPR02(II: 465-468).
IEEE DOI Reference
0211
BibRef
Sminchisescu, C.[Cristian],
Triggs, B.[Bill],
Building Roadmaps of Minima and Transitions in Visual Models,
IJCV(61), No. 1, January 2005, pp. 81-101.
WWW Version.
0410
BibRef
Earlier:
Building Roadmaps of Local Minima of Visual Models,
ECCV02(I: 566 ff.).
HTML Version.
0205Avoiding local minima in searching techniques.
BibRef
Richards, J.A.[John A.],
Jia, X.P.[Xiu-Ping],
A Dempster-Shafer Relaxation Approach to Context Classification,
GeoRS(45), No. 5, May 2007, pp. 1422-1431.
IEEE DOI Reference
0704
BibRef
Schellewald, C.[Christian],
Roth, S.[Stefan],
Schnorr, C.[Christoph],
Evaluation of a convex relaxation to a quadratic assignment matching
approach for relational object views,
IVC(25), No. 8, 1 August 2007, pp. 1301-1314.
WWW Version.
0706Quadratic assignment; Weighted graph matching; Combinatorial optimization;
Convex programming; Object recognition
BibRef
Werner, T.[Tomas],
A Linear Programming Approach to Max-Sum Problem: A Review,
PAMI(29), No. 7, July 2007, pp. 1165-1179.
IEEE DOI Reference
0706
Constraint Satisfaction. Maximization of a sum of binary functions.
Explore a formulation from early Russian paper.
BibRef
Werner, T.[Tomas],
High-arity interactions, polyhedral relaxations, and cutting plane
algorithm for soft constraint optimisation (MAP-MRF),
CVPR08(1-8).
IEEE DOI Reference
0806
BibRef
Werner, T.[Tomas],
Combinatorial constraints on multiple projections of a set of points,
ICCV03(1011-1016).
IEEE DOI Reference
0311
BibRef
Potetz, B.[Brian],
Lee, T.S.[Tai Sing],
Efficient belief propagation for higher-order cliques using linear
constraint nodes,
CVIU(112), No. 1, October 2008, pp. 39-54.
WWW Version.
0810
BibRef
Earlier: A1, Only:
Efficient Belief Propagation for Vision Using Linear Constraint Nodes,
CVPR07(1-8).
IEEE DOI Reference
0706Belief propagation; Higher-order cliques; Non-pairwise cliques; Factor graphs; Continuous Markov random fields
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Coito, F.J.[Fernando J.],
Lemos, J.M.[João M.],
Adaptive Optimization with Constraints:
Convergence and Oscillatory Behaviour,
IbPRIA05(II:19).
Springer DOI Reference
0509
BibRef
Yuille, A.L.[Alan L.],
A Double-Loop Algorithm to Minimize the Bethe Free Energy,
EMMCVPR02(3 ff.).
HTML Version.
0205
BibRef
Earlier:
A Double-Loop Algorithm to Minimize the Bethe and Kikuchi Free Energies,
SCTV01(xx-yy).
0106
BibRef
Yedidia, J.,
Freeman, W.T.,
Weiss, Y.,
Bethe free energy, Kikuchi approximations, and belief propagation
algorithms,
SCTV01(xx-yy).
0106Stable points of belief propagation algorithms for graphs
with loops correspond to extrema of the Bethe free energy.
BibRef
Haddon, J.,
Boyce, J.,
Spatio-Temporal Relaxation Labelling Applied to Segmented
Infrared Image Sequences,
ICPR96(II: 171-175).
IEEE DOI Reference
9608(Defence Res. Agency, UK)
BibRef
Horiuchi, T.,
Yamamoto, K.,
Yamada, H.,
Robust Relaxation Method for Structural Matching Under Uncertainty,
ICPR96(II: 176-180).
IEEE DOI Reference
9608(Univ. of Tsukuba, J)
BibRef
Shao, Z.,
Kittler, J.V.,
Fuzzy Non-Iterative ARG Labeling with Multiple Interpretations,
ICPR96(II: 181-185).
IEEE DOI Reference
9608(Univ. of Surrey, UK)
BibRef
Hatef, M.,
Kittler, J.V.,
Combining symbolic with numeric attributes in multi-class object
recognition problems,
ICIP95(III: 364-367).
IEEE DOI Reference
9510
BibRef
Deruyver, A.,
Hode, Y.,
Semantic graph and arc consistency in 'true' three dimensional image
labelling,
ICIP95(II: 619-622).
IEEE DOI Reference
9510
BibRef
Choate, J.A.,
Gennert, M.A.,
Multiscale relaxation labeling of fractal images,
CVPR93(674-675).
IEEE Abstract. IEEE Top Reference.
0403
BibRef
McLean, C.R.,
Dyer, C.R.,
An Analog Relaxation Processor,
ICPR80(58-60).
BibRef
8000
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Continuous Relaxation Theory .