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.
WWW Version.
0210
BibRef
Earlier:
A Hierarchical Framework for Spectral Correspondence,
ECCV02(I: 266 ff.).
HTML Version.
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 Abstract.
IEEE DOI Link
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:
Correspondence matching using Spectral Clusters,
SCIA01(P-W4A).
0206
BibRef
Earlier:
A hierarchical framework for modal correspondence matching,
CIAP01(327-332).
IEEE Top Reference.
0210
BibRef
Earlier:
An Improved Point Proximity Matrix for Modal Matching,
ICPR00(Vol II: 34-37).
IEEE DOI Link
0009
See also Feature-Based Correspondence: An Eigenvector Approach.
BibRef
Carcassoni, M.,
Hancock, E.R.,
Point-set alignment using multidimensional scaling,
ICPR02(II: 402-405).
IEEE DOI Link
0211
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 Link
WWW Version.
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 Link
0812
BibRef
Moss, S.[Simon],
Hancock, E.R.[Edwin R.],
Pose Clustering with Density Estimation and Structural Constraints,
CVPR99(II: 85-91).
IEEE Abstract.
IEEE DOI Link
BibRef
9900
Earlier:
Structural Constraints for Pose Clustering,
CAIP99(632-640).
WWW Version.
9909
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Relaxation Based Techniques .