12.1.6.1 Clustering and Accumulation Array Techniques

Chapter Contents (Back)
Matching, Points. Accumulation Matching. Matching, Accumulation. Hough.

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., Rosenfeld, A., and Danker, A.J.,
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. BibRef

Xie, M.,
Stereo and Motion Matching: A Hough-Transform Inspired Method,
PRL(15), No. 11, November 1994, pp. 1143-1150. BibRef 9411

Carcassoni, M.[Marco], Hancock, E.R.[Edwin R.],
Spectral correspondence for point pattern matching,
PR(36), No. 1, January 2003, pp. 193-204.
Elsevier DOI 0210
BibRef
Earlier:
A Hierarchical Framework for Spectral Correspondence,
ECCV02(I: 266 ff.).
Springer DOI 0205
BibRef
And:
Alignment using Spectral Clusters,
BMVC02(Poster Session). 0208
BibRef
Earlier:
Point Pattern Matching with Robust Spectral Correspondence,
CVPR00(I: 649-655).
IEEE DOI 0005
Spectral approach for graph matching. BibRef

Carcassoni, M.[Marco], Hancock, E.R.[Edwin R.],
Correspondence matching with modal clusters,
PAMI(25), No. 12, December 2003, pp. 1609-1615.
IEEE Abstract. 0401
BibRef
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.], Khorram, S.[Siamak],
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


Schönberger, J.L.[Johannes L.], Sinha, S.N.[Sudipta N.], Pollefeys, M.[Marc],
Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching,
ECCV18(XIII: 758-775).
Springer DOI 1810
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 .


Last update:Oct 15, 2018 at 09:19:25