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.
Elsevier DOI
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.
Elsevier DOI
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
Harlow, C.A.[Charles A.],
Image Analysis and Graphs,
CGIP(2), No. 1, August 1973, pp. 60-82.
Elsevier DOI
BibRef
7308
Cheng, J.K., and
Huang, T.S.,
Image Registration by Matching Relational Structures,
PR(17), No. 1, 1984, pp. 149-159.
Elsevier DOI
BibRef
8400
Earlier:
ICPR82(354-356).
BibRef
Earlier:
PRIP81(542-547).
Recognize Structures. Recognition is based on long edge segments of tools. Binary
relations are combined to produce ternary relations. Construct
relational descriptions of the boundary - very gross features -
long edges and chords of curved sections. The matching is based on
"stars" - nodes and every thing it is linked to, the compatibility
is overlap of possible assignments in pairs of stars. There is a
star for each node, with ratings for others stars, refinement
through relaxation (10 to 20 iterations).
BibRef
Cheng, J.K., and
Huang, T.S.,
A Subgraph Isomorphism Algorithm Using Resolution,
PR(13), No. 5, 1981, pp. 371-379.
Elsevier DOI
BibRef
8100
Cheng, J.K., and
Huang, T.S.,
Recognition of Curvilinear Objects by Matching
Relational Structures,
PRIP82(343-348).
BibRef
8200
Bolles, R.C., and
Cain, R.A.,
Recognizing and Locating Partially Visible Objects:
The Local-Feature-Focus Method,
IJRR(1), No. 3, Fall 1982, pp. 57-82.
BibRef
8200
Earlier: A1 only:
AAAI-80(41-43).
BibRef
And: A1, A2:
Recognizing and Locating Partially Visible Workpieces:
The Local-Feature-Focus Method,
PRIP82(498-503).
Recognize Two-Dimensional Objects. The location and matching is based on distinct features (holes and
corners) with a hypothesis formed from several consistent feature
locations. Testing the hypothesis yields more features and an
indication of occlusions. Find a feature, predict the location of
other features, (cluster) graph matching in applied to identify the
cluster, and then extract the rest of the object to verify the
identification.
BibRef
Bolles, R.C.,
Verification Vision for Programmable Assembly,
IJCAI77(569-575).
BibRef
7700
And:
Verification Vision within a Programmable Assembly System,
Stanford AI275, December 1975.
BibRef
Kupeev, K.Y.,
Wolfson, H.J.,
A New Method of Estimating Shape Similarity,
PRL(17), No. 8, July 1 1996, pp. 873-887.
9608
Perceptual similarity using contours.
PS File.
BibRef
Kupeev, K.Y., and
Wolfson, H.J.,
On Shape Similarity,
ICPR94(A:227-231).
IEEE DOI Perceptual similarity of contours for shape matching.
BibRef
9400
Fitch, A.J.,
Kadyrov, A.,
Christmas, W.J.,
Kittler, J.V.,
Fast robust correlation,
IP(14), No. 8, August 2005, pp. 1063-1073.
IEEE DOI
0508
BibRef
Earlier:
Fast exhaustive robust matching,
ICPR02(III: 903-906).
IEEE DOI
0211
BibRef
Ommer, B.[Björn],
Mader, T.[Theodor],
Buhmann, J.M.[Joachim M.],
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving
Camera,
IJCV(83), No. 1, June 2009, pp. xx-yy.
Springer DOI
0903
BibRef
Ommer, B.[Bjorn],
Buhmann, J.M.[Joachim M.],
Learning the Compositional Nature of Visual Object Categories for
Recognition,
PAMI(32), No. 3, March 2010, pp. 501-516.
IEEE DOI
1002
BibRef
Earlier:
Learning the Compositional Nature of Visual Objects,
CVPR07(1-8).
IEEE DOI
0706
BibRef
And:
Compositional Object Recognition, Segmentation, and Tracking in Video,
EMMCVPR07(318-333).
Springer DOI
0708
BibRef
Earlier:
Learning Compositional Categorization Models,
ECCV06(III: 316-329).
Springer DOI
0608
BibRef
Earlier:
Object Categorization by Compositional Graphical Models,
EMMCVPR05(235-250).
Springer DOI
0601
Learn composition of objects (of parts)
BibRef
Ommer, B.[Bjorn],
Sauter, M.[Michael],
Buhmann, J.M.[Joachim M.],
Learning Top-Down Grouping of Compositional Hierarchies for Recognition,
PercOrg06(194).
IEEE DOI
0609
BibRef
Roth, V.[Volker],
Ommer, B.[Björn],
Exploiting Low-Level Image Segmentation for Object Recognition,
DAGM06(11-20).
Springer DOI
0610
BibRef
Sadeghi, F.[Fereshteh],
Tappen, M.F.[Marshall F.],
Latent Pyramidal Regions for Recognizing Scenes,
ECCV12(V: 228-241).
Springer DOI
1210
BibRef
Silva, F.B.[Fernanda B.],
Tabbone, S.[Salvatore],
da Silva Torres, R.[Ricardo],
BoG: A New Approach for Graph Matching,
ICPR14(82-87)
IEEE DOI
1412
Accuracy; Dictionaries; Kernel; Training; Vectors; Visualization; Vocabulary
BibRef
Penatti, O.A.B.[Otávio A. B.],
Valle, E.[Eduardo],
da Silva Torres, R.[Ricardo],
Encoding Spatial Arrangement of Visual Words,
CIARP11(240-247).
Springer DOI
1111
BibRef
Peralta, B.[Billy],
Soto, A.[Alvaro],
Mixing Hierarchical Contexts for Object Recognition,
CIARP11(232-239).
Springer DOI
1111
Category level recognition.
BibRef
Yao, B.P.[Bang-Peng],
Niebles, J.C.[Juan Carlos],
Fei-Fei, L.[Li],
Mining discriminative adjectives and prepositions for natural scene
recognition,
VCL-ViSU09(100-106).
IEEE DOI
0906
Appearance and relations of patches.
BibRef
Shokoufandeh, A.[Ali],
Dickinson, S.J.[Sven J.],
Jönsson, C.[Clas],
Bretzner, L.[Lars],
Lindeberg, T.[Tony],
On the Representation and Matching of Qualitative Shape at Multiple
Scales,
ECCV02(III: 759 ff.).
Springer DOI
0205
Initial match of low level features.
BibRef
Yamaguchi, A.[Akashi],
Inokuchi, S.[Seiji],
Kochi, K.[Kazutaka],
Stereo Matching for Stone Statues Using SRI Parameters
and Relational Graph,
ICPR98(Vol I: 785-787).
IEEE DOI
9808
BibRef
Dubuisson-Jolly, M.P.[Marie-Pierre],
Jain, A.K.,
A Modified Hausdorff Distance for Object Matching,
ICPR94(A:566-568).
IEEE DOI
BibRef
9400
Enomoto, H.,
Yonezaki, N.,
Nitta, K.,
A Model for Perception of Structural Image Feature,
IJCAI79(257-259).
BibRef
7900
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Object Recognition, General Techniques .