Harlow, C.A.,
Image Analysis and Graphs,
CGIP(2), No. 1, August 1973, pp. 60-82.
WWW Version.
BibRef
7308
Winston, P.H.,
Learning Structural Descriptions from Examples,
PsychCV75(157-209). Chapter 5.
BibRef
7500
And:
Ph.D.Thesis (EE),
BibRef
MAC-TR-76, September, 1970.
BibRef
And:
MIT AI-TR-231, 1970.
WWW Version.
Learning. Matching network descriptions. Winston built on the work of
Guzman(
See also Computer Recognition of Three-Dimensional Objects in a Visual Scene. )
by using recognized blocks-world objects in a learning
system. Three-dimensional structures are represented using semantic
networks with elementary objects as a node and relations or
descriptions given by the arcs. Object descriptions are learned by
giving the system well selected examples that cause specializations or
generalizations of the description. This work avoids the very real
problem of extracting these descriptions from images, but provides a
good introduction to the issues of high level computer vision.
BibRef
Winston, P.H.,
Scene Understanding Systems,
FPR72(569-574), 1972.
BibRef
7200
Winston, P.H.,
Learning and Reasoning by Analogy,
CACM(23), No. 12, December 1980, pp. 689-703.
BibRef
8012
Earlier:
MIT AI Memo-520, April 1979.
BibRef
Winston, P.H.,
Learning New Principles from Precedents and Exercises,
AI(19), No. 3, November 1982, pp. 321-350.
WWW Version. Continuing learning, less on vision.
BibRef
8211
Winston, P.H.,
Binford, T.O.,
Katz, B., and
Lowry, M.,
Learning Physical Descriptions from Functional Definitions, Examples,
and Precedents,
RR-IS84(xx).
BibRef
8400
Earlier:
Learning Physical Descriptions from Functional Descriptions,
AAAI-83(433-439).
BibRef
Evans, T.G.,
A Heuristic Program to Solve Geometry-Analogy Problems,
SJCC1964, AFIPS, Vol. 25, pp. 5-16.
BibRef
6400
And:
RCV87(444-455).
Analogy. Graph descriptions of 2-D pictures.
BibRef
Barrow, H.G., and
Popplestone, R.J.,
Relational Descriptions in Picture Processing,
MI(VI), 1971, pp. 377-396.
Matching, Tree Search.
Classical work in structural description, matching and
segmentation. The region growing technique is intended to be an
incomplete, fast region grower. The basic idea is to collect points
that are similar (within 3 gray levels out of a total of 16) to
preselected grid points (a 16X16 grid over the original 64X64
image). These elementary regions may overlap. These elementary
regions are merged according to the contrast along the border.
This procedure also discards background regions (i.e. those which
touch the sides of the image). The simple region grower produces
the basic descritpion of the object. A structural (graph-based)
description is generated from properties of the regions (brightness
and shape) and relations between regions (adjacency, bigger,
distance between, and positional relations). The correspondence
between the model graph and the resulting image graph is determined
by a branch-and-bound tree searching technique.
See related segmentation work:
See also Scene Analysis Using Regions.
BibRef
7100
Barrow, H.G.,
Ambler, A.P., and
Burstall, R.M.,
Some Techniques for Recognizing Structures in Pictures,
FPR72(1-29).
BibRef
7200
CMetImAly77(397-425).
Matching, Graphs.
Recognize Structures. Another early classical work in structural matching.
BibRef
Ambler, A.P.,
Popplestone, R.J.,
Inferring the Position of Bodies from Specified Spatial Relationships,
AI(6), No. 2, June 1975, pp. 157-174.
WWW Version.
BibRef
7506
Popplestone, R.J.,
Ambler, A.P., and
Bellos, I.M.,
An Interpreter for a Language for Describing Assemblies,
AI(14), No. 1, August 1980, pp. 79-107.
WWW Version.
BibRef
8008
Barrow, H.G., and
Burstall, R.M.,
Subgraph Isomorphism, Matching Relational Structures and
Maximal Cliques,
IPL(4), 1976, pp. 83-84.
Association Graph.
BibRef
7600
Pavlidis, T.,
Representation of Figures by Labeled Graphs,
PR(4), No. 1, January 1972, pp. 5-17.
WWW Version.
BibRef
7201
Fischler, M.A., and
Elschlager, R.A.[Robert A.],
The Representation and Matching of Pictorial Structures,
TC(22), No. 1, January, 1973, pp. 67-92.
BibRef
7301
And:
CMetImAly77(31-56).
Deformable Template. Early good paper using springs between nodes in the graph.
BibRef
Fischler, M.A.,
On the Representation of Natural Scenes,
CVS78(47-52).
BibRef
7800
Firschein, O., and
Fischler, M.A.,
Describing and Abstracting Pictorial Structures,
PR(3), No. 4, November 1971, pp. 421-434.
WWW Version.
BibRef
7111
Firschein, O.,
Fischler, M.A.,
A study in descriptive representation of pictorial data,
PR(4), No. 4, December 1972, pp. 361-366.
WWW Version.
0309Attempt at general descriptions for general analysis.
BibRef
Ram, G.,
Analysis of Images Specified by Graphlike Descriptions,
CGIP(5), 1976, pp. 137-148.
BibRef
7600
Cohen, B.L.,
A Powerful and Efficient Structural Pattern Recognition System,
AI(9), No. 3, December 1977, pp. 223-255.
WWW Version.
BibRef
7712
Giustini, R.G.,
Levine, M.D.,
Malowany, A.S.,
Picture Generation Using Semantic Nets,
CGIP(7), No. 1, February 1978, pp. 1-29.
WWW Version.
BibRef
7802
Itai, A.,
Rodeh, M.,
Tanimoto, S.L.,
Some Matching Problems for Bipartite Graphs,
JACM(25), 1978, pp. 517-525.
BibRef
7800
Funt, B.V.,
Problem Solving with Diagrammatic Representations,
AI(13), No. 3, May 1980, pp. 201-230.
WWW Version.
BibRef
8005
Earlier:
Whisper: A Problem-Solving System Utilizing Diagrams and a Parallel
Processing Retina,
IJCAI77(459-464).
BibRef
Levine, M.D.,
Ting, D.,
Intermediate Level Picture Interpretation Using
Complete Two-Dimensional Models,
CGIP(16), No. 3, July 1981, pp. 185-209.
WWW Version.
BibRef
8107
Kodratoff, Y.[Yves],
Generation and semantics of patterns in a discrete space,
CGIP(5), No. 4, December 1976, pp. 447-458.
WWW Version.
0501
BibRef
Kodratoff, Y.,
Lemerle-Loisel, R.,
Learning Complex Structural Descriptions from Examples,
CVGIP(27), No. 3, September 1984, pp. 266-290.
WWW Version.
BibRef
8409
Earlier:
IJCAI81(141-143).
BibRef
Krose, B.J.A.,
A Structure Description of Visual Information,
PRL(3), 1985, pp. 41-50.
BibRef
8500
Werman, M.,
Peleg, S.,
Melter, R., and
Kong, T.Y.,
Bipartite Graph Matching for Points on a Line or a Circle,
Algorithms(7), 1986, pp. 277-284.
BibRef
8600
Niemann, H.,
Sagerer, G.F.,
Schroder, S., and
Kummert, F.,
ERNEST: A Semantic Network System for Pattern Understanding,
PAMI(12), No. 9, September 1990, pp. 883-905.
IEEE Abstract. IEEE Top Reference.
WWW Version. Discusses the graph structure for matching and how to use a general
graph matching system. A lot is fairly standard, except that it is
general.
BibRef
9009
Bauckhage, C.[Christian],
Kummert, F.[Franz],
Sagerer, G.F.[Gerhard F.],
A Structural Framework for Assembly Modeling and Recognition,
CAIP03(49-56).
WWW Version.
0311
BibRef
Hanheide, M.,
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ICPR04(II: 459-462).
WWW Version.
0409
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Niemann, H.,
Sagerer, G.F.,
Eichhorn, W.,
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PRAI(2), 1988, pp. 557-572.
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8800
Niemann, H.,
A Homogeneous Architecture for Knowledge Based
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CAIA85(88-93).
BibRef
8500
Eshera, M.A.,
Fu, K.S.,
An Image Understanding System Using Attributed Symbolic
Representation and Inexact Graph-Matching,
PAMI(8), No. 5, September 1986, pp. 604-618.
Generate graphs with labeled arcs and features and match.
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8609
Tsai, W.H., and
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Subgraph Error-Correcting Isomorphisms for
Syntatic Pattern Recognition,
SMC(13), No. 1, January-February 1983, pp. 48-62.
BibRef
8301
Tsai, W.H., and
Fu, K.S.,
Error-Correcting Isomorphisms of Attributed
Relational Graphs for Pattern Analysis,
SMC(9), No. 12, December 1979, pp. 757-768.
Still an O(l^3n^2) method.
BibRef
7912
Goel, A.,
Bylander, T.,
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PAMI(11), No. 12, December 1989, pp. 1312-1316.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0401
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Suganuma, Y.[Yoshinori],
Learning Structures of Visual Patterns from Single Instances,
AI(50), No. 1, June 1991, pp. 1-36.
WWW Version.
BibRef
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Blake, R.E.,
Partitioning Graph Matching with Constraints,
PR(27), No. 3, March 1994, pp. 439-446.
WWW Version.
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Caelli, T.M.[Terry M.],
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IEEE Abstract. IEEE Top Reference.
0403
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WWW Version.
0501
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0408
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ICPR04(II: 471-474).
WWW Version.
0409
BibRef
Caetano, T.S.[Tiberio S.],
Cheng, L.[Li],
Le, Q.V.[Quoc V.],
Smola, A.J.[Alex J.],
Learning Graph Matching,
ICCV07(1-8).
WWW Version.
0710
BibRef
Bunke, H.,
Inexact Graph Matching for Structural Pattern Recognition,
PRL(1), No. 4, 1983, pp. 245-253.
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Günter, S.[Simon],
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Self-organizing map for clustering in the graph domain,
PRL(23), No. 4, February 2002, pp. 405-417.
HTML Version.
0202
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WWW Version.
0304
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Wong, E.K.,
Model Matching in Robot Vision by Subgraph Isomorphism,
PR(25), No. 3, March 1992, pp. 287-303.
WWW Version.
BibRef
9203
de Piero, F.W.,
Trivedi, M.M.,
Serbin, S.,
Graph Matching Using a Direct Classification of Node Attendance,
PR(29), No. 6, June 1996, pp. 1031-1048.
WWW Version.
9606
BibRef
Kasif, S.,
Kitchen, L.,
Rosenfeld, A.,
A Hough Transform Technique for Subgraph Isomorphism,
PRL(2), 1983, pp. 83-88.
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Tang, Y.C.,
Lee, C.S.G.,
Optimal Strategic Recognition of Objects Based on
Candidate Discriminating Graph with Coordinated Sensors,
SMC(22), 1992, pp. 647-661.
BibRef
9200
Cross, A.D.J.,
Wilson, R.C.,
Hancock, E.R.,
Inexact Graph Matching Using Genetic Search,
PR(30), No. 6, June 1997, pp. 953-970.
WWW Version.
9706
BibRef
Earlier:
Genetic Search for Structural Matching,
ECCV96(I:514-525).
WWW Version.
BibRef
Cross, A.D.J.[Andrew D.J.],
Hancock, E.R.[Edwin R.],
Graph Matching with a Dual-Step EM Algorithm,
PAMI(20), No. 11, November 1998, pp. 1236-1253.
IEEE Abstract. IEEE Top Reference.
WWW Version.
9811
BibRef
Earlier:
Perspective matching using the EM algorithm,
CIAP97(I: 406-413).
WWW Version.
9709
BibRef
Wilson, R.C.[Richard C.],
Cross, A.D.J.[Andrew D.J.],
Hancock, E.R.[Edwin R.],
Structural Matching with Active Triangulations,
CVIU(72), No. 1, October 1998, pp. 21-38.
WWW Version.
BibRef
9810
Torsello, A.[Andrea],
Hancock, E.R.[Edwin R.],
Learning Shape-Classes Using a Mixture of Tree-Unions,
PAMI(28), No. 6, June 2006, pp. 954-967.
WWW Version.
0605
BibRef
Earlier:
Learning Mixtures of Weighted Tree-Unions by Minimizing Description
Length,
ECCV04(Vol III: 13-25).
WWW Version.
0405
BibRef
Earlier:
Graph Clustering with Tree-Unions,
CAIP03(451-459).
WWW Version.
0311
BibRef
Earlier:
Shape-space from tree-union,
ICPR02(I: 188-191).
WWW Version.
0211edit operations produce the trees.
See also Skeletal Measure of 2D Shape Similarity, A.
BibRef
Torsello, A.[Andrea],
Hancock, E.R.[Edwin R.],
Graph embedding using tree edit-union,
PR(40), No. 5, May 2007, pp. 1393-1405.
WWW Version.
07022D shape; Skeleton; Tree-union; Embedding
See also Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering.
BibRef
Torsello, A.[Andrea],
An importance sampling approach to learning structural representations
of shape,
CVPR08(1-7).
WWW Version.
0806
BibRef
Xiao, B.[Bai],
Hancock, E.R.[Edwin R.],
A Spectral Generative Model for Graph Structure,
SSPR06(173-181).
WWW Version.
0608
BibRef
Earlier:
Geometric Characterisation of Graphs,
CIAP05(471-478).
WWW Version.
0509
BibRef
Xiao, B.[Bai],
Yu, H.[Hang],
Hancock, E.R.[Edwin R.],
Graph Matching Using Manifold Embedding,
ICIAR04(I: 352-359).
WWW Version.
0409
BibRef
Xiao, B.[Bai],
Hancock, E.R.[Edwin R.],
Trace Formula Analysis of Graphs,
SSPR06(306-313).
WWW Version.
0608
BibRef
Sagerer, G.F.[Gerhard F.],
Niemann, H.[Heinrich],
Semantic Networks for Understanding Scenes,
Plenum1997.
ISBN 0-306-45704-0. 512 pp.
Segmentation, Knowledge representation, Judgment, Control, Acquisition
of Knowledge, Explanation and User Interface, Applications.
BibRef
9700
Niemann, H.,
Hierarchical Graphs in Pattern Analysis,
ICPR80(213-216).
BibRef
8000
Bunke, H.,
Sagerer, G.F.,
Use and Representation of Knowledge in Image Understanding Based on
Semantic Networks,
ICPR84(1135-1137).
BibRef
8400
El-Sonbaty, Y.[Yasser],
Ismail, M.A.,
A New Algorithm for Subgraph Optimal Isomorphism,
PR(31), No. 2, February 1998, pp. 205-218.
WWW Version.
9802
BibRef
Earlier:
A Graph-Decomposition Algorithm for Graph Optimal Monomorphism,
BMVC97(xx-yy).
HTML Version.
0209
BibRef
Finch, A.M.[Andrew M.],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Symbolic graph matching with the EM algorithm,
PR(31), No. 11, November 1998, pp. 1777-1790.
WWW Version.
BibRef
9811
Williams, M.L.[Mark L.],
Wilson, R.C.[Richard C.],
Hancock, E.R.[Edwin R.],
Deterministic search for relational graph matching,
PR(32), No. 7, July 1999, pp. 1255-1271.
WWW Version.
BibRef
9907
Jiang, X.Y.[Xiao-Yi],
Bunke, H.[Horst],
Optimal quadratic-time isomorphism of ordered graphs,
PR(32), No. 7, July 1999, pp. 1273-1283.
WWW Version.
BibRef
9907
Pelillo, M.[Marcello],
Siddiqi, K.[Kaleem],
Zucker, S.W.[Steven W.],
Matching Hierarchical Structures Using Association Graphs,
PAMI(21), No. 11, November 1999, pp. 1105-1120.
IEEE Abstract. IEEE Top Reference.
WWW Version.
9912
BibRef
Earlier:
ECCV98(II: 3).
WWW Version.
BibRef
And:
Attributed tree matching and maximum weight cliques,
CIAP99(1154-1159).
WWW Version.
9909When trees are hierarchical find maximal cliques may not work. Recast the
matching problem as a quadratic program.
BibRef
Pelillo, M.[Marcello],
Siddiqi, K.[Kaleem],
Zucker, S.W.[Steven W.],
Many-to-many Matching of Attributed Trees Using Association Graphs and
Game Dynamics,
VF01(583 ff.).
HTML Version.
0209
BibRef
Pelillo, M.[Marcello],
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics,
PAMI(24), No. 11, November 2002, pp. 1535-1541.
IEEE Abstract. IEEE Top Reference.
0211
BibRef
Earlier:
EMMCVPR02(423 ff.).
HTML Version.
0205
BibRef
Pelillo, M.[Marcello],
A Unifying Framework for Relational Structure Matching,
ICPR98(Vol II: 1316-1319).
WWW Version.
9808
BibRef
Torsello, A.[Andrea],
Hidovic-Rowe, D.[Dzena],
Pelillo, M.[Marcello],
Polynomial-Time Metrics for Attributed Trees,
PAMI(27), No. 7, July 2005, pp. 1087-1099.
IEEE Abstract. IEEE Top Reference.
0506
BibRef
Earlier:
A Polynomial-Time Metric for Attributed Trees,
ECCV04(Vol IV: 414-427).
WWW Version.
0405
BibRef
And:
Four metrics for efficiently comparing attributed trees,
ICPR04(II: 467-470).
WWW Version.
0409Four distance measures centered around the notion of
a maximal similarity common subtree.
BibRef
Torsello, A.[Andrea],
Albarelli, A.[Andrea],
Pelillo, M.[Marcello],
Matching Relational Structures using the Edge-Association Graph,
CIAP07(775-780).
WWW Version.
0709
BibRef
Bunke, H.,
Kandel, A.,
Mean and maximum common subgraph of two graphs,
PRL(21), No. 2, February 2000, pp. 163-168.
0003
BibRef
van Wyk, M.A.[Michaël A.],
Durrani, T.S.[Tariq S.],
van Wyk, B.J.[Barend J.],
A RKHS Interpolator-Based Graph Matching Algorithm,
PAMI(24), No. 7, July 2002, pp. 988-995.
IEEE Abstract. IEEE Top Reference.
0207Graph matching for lines from aerial images.
BibRef
Toudjeu, I.T.[Ignace Tchangou],
van Wyk, B.J.[Barend Jacobus],
van Wyk, M.A.[Michaël Antonie],
van den Bergh, F.[Frans],
Global Image Feature Extraction Using Slope Pattern Spectra,
ICIAR08(xx-yy).
WWW Version.
0806
BibRef
van Wyk, M.A.[Michaël A.],
Durrani, T.S.[Tariq S.],
A Framework for Multi-Scale and Hybrid RKHS-Based Approximators,
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HTML Version.
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Park, B.G.[Bo Gun],
Lee, K.M.[Kyoung Mu],
Lee, S.U.[Sang Uk],
Lee, J.H.[Jin Hak],
Recognition of partially occluded objects using probabilistic
ARG-based matching,
CVIU(90), No. 3, June 2003, pp. 217-241.
WWW Version.
0307Attributed Relational Graph
BibRef
Park, B.G.[Bo Gun],
Lee, K.M.[Kyoung Mu],
Lee, S.U.[Sang Uk],
A Novel Stochastic Attributed Relational Graph Matching Based on
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ACIVS06(978-989).
WWW Version.
0609
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van Wyk, B.J.,
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Kronecker product graph matching,
PR(36), No. 9, September 2003, pp. 2019-2030.
WWW Version.
0307
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Sangineto, E.[Enver],
An abstract representation of geometric knowledge for object
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PRL(24), No. 9-10, June 2003, pp. 1241-1250.
WWW Version.
0304Efficient algorithm for constraint satisfaction.
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He, L.[Lei],
Han, C.Y.[Chia Y.],
Everding, B.[Bryan],
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Graph matching for object recognition and recovery,
PR(37), No. 7, July 2004, pp. 1557-1560.
WWW Version.
0405
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Lopresti, D.P.,
Wilfong, G.,
A fast technique for comparing graph representations with applications
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IJDAR(6), No. 4, April 2004, pp. 219-229.
WWW Version.
0406Document analysis application.
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Gori, M.,
Maggini, M.,
Sarti, L.,
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PAMI(27), No. 7, July 2005, pp. 1100-1111.
IEEE Abstract. IEEE Top Reference.
0506
BibRef
Earlier:
Graph matching using random walks,
ICPR04(III: 394-397).
WWW Version.
0409
BibRef
Frey, B.J.[Brendan J.],
Jojic, N.[Nebojsa],
A Comparison of Algorithms for Inference and Learning in Probabilistic
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WWW Version.
0508Graph models of the image.
BibRef
Todorovic, S.[Sinisa],
Nechyba, M.C.[Michael C.],
Dynamic Trees for Unsupervised Segmentation and Matching of Image
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PAMI(27), No. 11, November 2005, pp. 1762-1777.
WWW Version.
0510
BibRef
Earlier:
Detection of artificial structures in natural-scene images using dynamic
trees,
ICPR04(I: 35-39).
WWW Version.
0409Segment the image for matching. Captures relations (components).
BibRef
Todorovic, S.[Sinisa],
Nechyba, M.C.[Michael C.],
Interpretation of complex scenes using dynamic tree-structure Bayesian
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CVIU(106), No. 1, April 2007, pp. 71-84.
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0704
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And:
Interpretation of Complex Scenes Using Generative Dynamic-Structure
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GenModel04(184).
WWW Version.
0406
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Earlier:
Multiresolution linear discriminant analysis: efficient extraction of
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ICIP03(I: 1029-1032).
IEEE Abstract. IEEE Top Reference.
0312Generative models; Bayesian networks; Dynamic trees;
Variational inference; Image segmentation; Object recognition
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Todorovic, S.[Sinisa],
Ahuja, N.[Narendra],
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IJCV(78), No. 1, June 2008, pp. 47-66.
WWW Version.
0803
BibRef
Earlier:
Extracting Subimages of an Unknown Category from a Set of Images,
CVPR06(I: 927-934).
WWW Version.
0606
BibRef
And: A2, A1:
Learning the Taxonomy and Models of Categories Present in Arbitrary
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ICCV07(1-8).
WWW Version.
0710Identify properties of the object, learn a model, segment.
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Ahuja, N.[Narendra],
Todorovic, S.[Sinisa],
Connected Segmentation Tree:
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CVPR08(1-8).
WWW Version.
0806
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Todorovic, S.[Sinisa],
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CVPR08(1-8).
WWW Version.
0806
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Kumar, S.[Sanjiv],
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Discriminative Random Fields,
IJCV(68), No. 2, June 2006, pp. 179-201.
WWW Version.
0606
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Earlier:
Discriminative random fields: a discriminative framework for contextual
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ICCV03(1150-1157).
WWW Version.
0311Classify regions given a single image.
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EMMCVPR05(153-168).
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0601
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Naik, S.K.[Sarif Kumar],
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Distinct Multicolored Region Descriptors for Object Recognition,
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0706Color features of regions for recognition.
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GS07(177-194).
WWW Version.
0711
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Learning Compositional Categorization Models,
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Object Categorization by Compositional Graphical Models,
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Object Ontology,
ICIAR05(473-480).
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CVPR05(I: 672-679).
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0507
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Single-example learning of novel classes using representation by
similarity,
BMVC05(xx-yy).
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Bai, X.[Xiao],
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Graph Matching using Spectral Embedding and Semidefinite Programming,
BMVC04(xx-yy).
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Luo, B.[Bin],
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Graph based image matching,
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Attribute Trees In Image Analysis:
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Takahashi, K.,
A fast structural matching and its application to pattern analysis of
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ICIP98(III: 804-808).
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Dynamic Link Matching for Multiple Object Recognition,
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Shapiro, L.G.,
Triplet-Based Object Recognition Using Synthetic and
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ICPR96(IV: 75-79).
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9608(Univ. of Washington, USA)
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Markov Random Field Modeling in Computer Vision,
New York:
Springer-Verlag1995.
260 pp.
ISBN 0-387-70145-1.
Or: (US) ISBN 4-431-70145-1.
HTML Version. Or:
HTML Version. Markov random field (MRF) theory provides a basis for modeling contextual
constraints in visual processing and interpretation.
Topics include:
introduction to fundamental theories, formulations
of MRF vision models, MRF parameter estimation, and optimization algorithms.
Various vision models are presented in a unified framework, including image
restoration and reconstruction, edge and region segmentation, texture, stereo
and motion, object matching and recognition, and pose estimation.
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Wang, C.H.[Cai-Hua],
Abe, K.[Keiichi],
Region Correspondence by Inexact Attributed Planar Graph Matching,
ICCV95(440-447).
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9500
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Abe, K.[Keiichi],
Region Correspondence for Color Scene Images Taken
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MVA94(26-29).
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Structural patterns or discrete events? A link between pattern
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9208
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2D objects recognition by graph matching,
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Esposito, F.,
Malerba, D.,
Semeraro, G.,
Flexible Matching for Noisy Structural Descriptions,
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A matching algorithm based on hierarchical primitive structure,
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Object Recognition Using Relational Clique and Cycle Mappings,
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8811
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Granger, C.,
Symbolic Scene Matching,
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Khan, N.A.,
Jain, R.,
Matching an Imprecise Object Description with Models in a
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Tanimoto, S.L., and
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Graph Labelling Algorithms for Picture Analysis,
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Matching Graphs and 3-D Network Descriptions .