Bolles, R.C.[Robert C.],
Horaud, P.[Patrice], and
Hannah, M.J.[Marsha Jo],
3DPO: A Three-Dimensional Part Orientation System,
IJRR(5), No. 3, Fall 1986, pp. 3-26.
BibRef
8600
And:
IJCAI83(1116-1120) reprinted in
BibRef
RCV87(355-359).
BibRef
And: A1, A2, Only:
3DMV87(399-450).
Light stripe 3D data is used for input. Locate primitive features,
cluster these, generate and verify the
hypothesis of match, generate transformations.
BibRef
Horaud, P.[Patrice], and
Bolles, R.C.[Robert C.],
3DPO's Strategy for Matching Three-Dimensional Objects in Range Data,
Conf. on RoboticsAtlanta, March 1984, pp. 78-85.
BibRef
8403
Kahl, D.J.,
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Some Experiments in Point Pattern Matching,
SMC(10), No. 2, February 1980, pp. 105-116.
BibRef
8002
UMD-TR-690, September 1978.
Point features are used to find a global transform (translation only)
between two images of the same scene. Different numbers of feature
points may be found in the two images, but the distortions and
rotations are small. For each pair of points in both images, a
translation is computed to map the first point in one pair to the
first point in the other pair. If the translation also approximately
maps the second points in the pairs then the rating of this possible
translation is incremented. The best global translation is indicated
by a high rating (or a cluster of high ratings) in the accumulation
space. This technique is sensitive to displacement noise, but
tolerates deletions or additions of points. Since the global
accumulation covers only translation, changes in orientation
(rotation) also cause problems. Some error tolerance is possible by
introducing labels (or property values) for each feature point.
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Xie, M.,
Stereo and Motion Matching: A Hough-Transform Inspired Method,
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Spectral correspondence for point pattern matching,
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Elsevier DOI
0210
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Earlier:
A Hierarchical Framework for Spectral Correspondence,
ECCV02(I: 266 ff.).
Springer DOI
0205
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And:
Alignment using Spectral Clusters,
BMVC02(Poster Session).
0208
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Earlier:
Point Pattern Matching with Robust Spectral Correspondence,
CVPR00(I: 649-655).
IEEE DOI
0005
Spectral approach for graph matching.
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Carcassoni, M.[Marco],
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Correspondence matching with modal clusters,
PAMI(25), No. 12, December 2003, pp. 1609-1615.
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0401
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Earlier:
Point-set alignment using multidimensional scaling,
ICPR02(II: 402-405).
IEEE DOI
0211
BibRef
Earlier:
Correspondence matching using Spectral Clusters,
SCIA01(P-W4A).
0206
BibRef
Earlier:
A hierarchical framework for modal correspondence matching,
CIAP01(327-332).
IEEE DOI
0210
BibRef
Earlier:
An Improved Point Proximity Matrix for Modal Matching,
ICPR00(Vol II: 34-37).
IEEE DOI
0009
See also Feature-Based Correspondence: An Eigenvector Approach.
BibRef
Haseeb, M.[Muhammad],
Hancock, E.R.[Edwin R.],
Eigenvector Sign Correction for Spectral Correspondence Matching,
CIAP13(II:41-50).
Springer DOI
1309
BibRef
Krish, K.[Karthik],
Heinrich, S.[Stuart],
Snyder, W.E.[Wesley E.],
Cakir, H.I.[Halil I.],
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Global registration of overlapping images using accumulative image
features,
PRL(31), No. 2, 15 January 2010, pp. 112-118.
Elsevier DOI
1001
Image registration; Feature matching; Accumulator-based methods;
Feature correspondence; Evidence accumulation
BibRef
Krish, K.[Karthik],
Snyder, W.E.[Wesley E.],
A New Accumulator-Based Approach to Shape Recognition,
ISVC08(II: 157-169).
Springer DOI
0812
BibRef
Chen, H.Y.[Hsin-Yi],
Lin, Y.Y.[Yen-Yu],
Chen, B.Y.[Bing-Yu],
Co-Segmentation Guided Hough Transform for Robust Feature Matching,
PAMI(37), No. 12, December 2015, pp. 2388-2401.
IEEE DOI
1512
BibRef
Earlier:
Robust Feature Matching with Alternate Hough and Inverted Hough
Transforms,
CVPR13(2762-2769)
IEEE DOI
1309
Hough transform; correspondence enrichment; point matching
BibRef
Wei, H.[Hui],
Yu, Q.[Qian],
Yang, C.Z.[Cheng-Zhuan],
Shape-based object recognition via Evidence Accumulation Inference,
PRL(77), No. 1, 2016, pp. 42-49.
Elsevier DOI
1606
Shape-based object recognition
BibRef
Lv, G.H.[Guo-Hua],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Enhancing SIFT-based image registration performance by building and
selecting highly discriminating descriptors,
PRL(84), No. 1, 2016, pp. 156-162.
Elsevier DOI
1612
BibRef
And:
A Novel Multi-Modal Image Registration Method Based on Corners,
DICTA14(1-8)
IEEE DOI
1502
image registration
BibRef
Lv, G.H.[Guo-Hua],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Lackmann, M.[Martin],
Detection of Structural Similarity for Multimodal Microscopic Image
Registration,
DICTA13(1-8)
IEEE DOI
1402
image colour analysis
BibRef
Hossain, M.T.[M. Tanvir],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Achieving High Multi-Modal Registration Performance Using Simplified
Hough-Transform with Improved Symmetric-SIFT,
DICTA12(1-7).
IEEE DOI
1303
BibRef
Hossain, M.T.[M. Tanvir],
Lv, G.H.[Guo-Hua],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Lackmann, M.[Martin],
Improved Symmetric-SIFT for Multi-modal Image Registration,
DICTA11(197-202).
IEEE DOI
1205
BibRef
Sadat, R.M.N.[Rafi M. Najmus],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Image registration using modified Local Ternary Pattern,
IVCNZ10(1-8).
IEEE DOI
1203
BibRef
Hossain, M.T.[M. Tanvir],
Teng, S.W.[Shyh Wei],
Lu, G.J.[Guo-Jun],
Lackmann, M.[Martin],
An Enhancement to SIFT-Based Techniques for Image Registration,
DICTA10(166-171).
IEEE DOI
1012
BibRef
Min, J.[Juhong],
Kim, S.[Seungwook],
Cho, M.[Minsu],
Convolutional Hough Matching Networks for Robust and Efficient Visual
Correspondence,
PAMI(45), No. 7, July 2023, pp. 8159-8175.
IEEE DOI
2306
Convolution, Kernel, Visualization, Transforms, Correlation, Tensors,
Semantics, Semantic visual correspondence, Hough matching,
center-pivot convolution
BibRef
Yörük, E.,
Öner, K.T.,
Akgül, C.B.,
An efficient Hough transform for multi-instance object recognition
and pose estimation,
ICPR16(1352-1357)
IEEE DOI
1705
Feature extraction, Histograms, Image recognition, Pose estimation,
Robustness, Transforms, Visualization, Feature Matching,
Hough Transform, Object Instance Recognition, Pose Estimation,
Product, Recognition
BibRef
Gonzalez, R.,
Registration of sheared images using phase correlation,
IVCNZ13(477-482)
IEEE DOI
1402
Hough transforms
BibRef
Zhao, S.B.[Shu-Bin],
Image Registration by Simulating Human Vision,
PSIVT07(692-701).
Springer DOI
0712
BibRef
Earlier:
Hough-Domain Image Registration By Metaheuristics,
ICARCV06(1-5).
IEEE DOI
0612
BibRef
Moss, S.[Simon],
Hancock, E.R.[Edwin R.],
Image registration with shape mixtures,
CIAP97(II: 172-179).
Springer DOI
9709
BibRef
Moss, S.[Simon],
Hancock, E.R.[Edwin R.],
Pose Clustering with Density Estimation and Structural Constraints,
CVPR99(II: 85-91).
IEEE DOI
BibRef
9900
Earlier:
Structural Constraints for Pose Clustering,
CAIP99(632-640).
Springer DOI
9909
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Relaxation Based Techniques .