Ranade, S., and
Rosenfeld, A.,
Point Pattern Matching by Relaxation,
PR(12), No. 4, 1980, pp. 269-275.
Elsevier DOI
Relaxation.
The input is two sets of points, each corresponding to feature
locations in different views of the scene.
Like Kahl(
See also Some Experiments in Point Pattern Matching. ), the system
finds a global displacement (translation) that best fits the data, but
works better with small rotation and scale changes. Points are
matched with all points in the other image with the match rating based
on how many other point matches agree with the transform computed from
that match. The scores are computed (and updated) based on the scores
of the other point pairs to produce a highly rated consensus transform
for the set of points.
BibRef
8000
Wang, C.Y.[Cheng-Ye],
Sun, H.F.[Han-Fang],
Yada, S.[Shiro], and
Rosenfeld, A.,
Some Experiments in Relaxation Image Matching Using Corner
Features,
PR(16), No. 2, 1983, pp. 167-182.
Elsevier DOI
BibRef
8300
Earlier:
UMD-CS TR-1-71.
Relaxation. A relaxation procedure is used to find matches between pairs of
images that differ in position and orientation. The matching is
performed on sets of feature points (corners), which have position,
orientation, contrast, and sharpness. After several iterations,
good matches are clustered which gives sets of transformations
(translation and rotation). The best transformation can be
selected from these likely ones. This extends an earlier method
See also Point Pattern Matching by Relaxation.
BibRef
Ogawa, H.[Hideo],
Labeled Point Pattern Matching by Fuzzy Relaxation,
PR(17), No. 5, 1984, pp. 569-573.
Elsevier DOI
BibRef
8400
Ogawa, H.[Hideo],
Labeled Point Pattern Matching by Delaunay Triangulation and
Maximal Cliques,
PR(19), No. 1, 1986, pp. 35-40.
Elsevier DOI
BibRef
8600
Kitchen, L.,
Relaxation for Point-Pattern Matching:
What it really Computes,
CVPR85(405-407).
Univ. of Massachusetts. Preliminary.
BibRef
8500
Sang, N.,
Zhang, T.X.,
Rotation and Scale Change Invariant Point Pattern Relaxation
Matching by the Hopfield Neural Network,
OptEng(36), No. 12, December 1997, pp. 3378-3385.
9801
BibRef
Mohanty, N.C.,
Computer Tracking of Moving Point Targets in Space,
PAMI(3), No. 5, September 1981, pp. 606-611.
BibRef
8109
Sethi, I.K., and
Jain, R.C.,
Finding Trajectories of Feature Points in a
Monocular Image Sequence,
PAMI(9), No. 1, January 1987, pp. 56-73.
BibRef
8701
Earlier:
CAIA85(106-111).
BibRef
And: A2, A1:
Establishing Correspondence of Non-Rigid Objects Using
Smoothness of Motion,
CVWS84(83-87).
This is primarily a long sequence correspondence problem. Initial
matches are generated based on nearest neighbors, then matches are
exchanged until it is stable. Exchanges are made to increase the
smoothness of motion criteria.
BibRef
Chen, H.H., and
Huang, T.S.,
Using Motion from Orthographic Views to Verify
3-D Point Matches,
PAMI(13), No. 9, September 1991, pp. 872-878.
IEEE DOI
BibRef
9109
Earlier:
Using Motion from Orthographic Projections to Prune
3-D Point Matches,
Motion89(290-297).
Matching, Sequence. Since it is orthographic, ignore Z and use only the X
and Y components.
See also Motion and Structure from Orthographic Projections.
BibRef
Bruckstein, A.M.[Alfred M.],
Netravali, A.N.[Arun N.],
On Minimal Energy Trajectories,
CVGIP(49), No. 3, March 1990, pp. 283-296.
Elsevier DOI Also see curve fitting papers (snakes and the like).
BibRef
9003
Salari, V., and
Sethi, I.K.,
Feature Point Correspondence in the Presence of Occlusion,
PAMI(12), No. 1, January 1990, pp. 87-91.
IEEE DOI
BibRef
9001
Earlier:
Correspondence in Presence of Occlusion,
CVWS87(327-330).
A modification of
See also Finding Trajectories of Feature Points in a Monocular Image Sequence.
BibRef
Sethi, I.K.,
Salari, V.,
Vemuri, S.,
Feature Point Matching in Image Sequences,
PRL(7), 1988, pp. 113-121.
BibRef
8800
Sethi, I.K.,
Salari, V.,
Vemuri, S.,
Image Sequence Segmentation Using Motion Coherence,
ICCV87(667-671).
BibRef
8700
Sethi, I.K.[Ishwar K.],
Patel, N.V.[Nilesh V.],
Yoo, J.H.[Jae H.],
A General Approach for Token Correspondence,
PR(27), No. 12, December 1994, pp. 1775-1786.
Elsevier DOI
BibRef
9412
Sethi, I.K.,
Ramesh, N.,
Local Association Based Recognition of Two-Dimensional Objects,
MVA(5), 1992, pp. 265-276.
BibRef
9200
Aloimonos, Y.F.[Yi-Fannis],
Tsakiris, D.P.[Dimitris P.],
On the Visual Mathematics of Tracking,
IVC(9), No. 4, August 1991, pp. 235-251.
Elsevier DOI
BibRef
9108
Earlier:
Tracking in a complex visual environment,
ECCV90(247-258).
Springer DOI
9004
Tracking known shape with rigid motion.
BibRef
Sudhir, G.,
Banerjee, S.,
Zisserman, A.,
Finding Point Correspondences in Motion Sequences Preserving
Affine Structure,
CVIU(68), No. 2, November 1997, pp. 237-246.
DOI Link
9712
BibRef
Earlier:
BMVC93(xx-yy).
PDF File.
BibRef
Mehrotra, R.[Rajiv],
Establishing Motion-Based Feature Point Correspondence,
PR(31), No. 1, January 1998, pp. 23-30.
Elsevier DOI
9802
BibRef
Cheng, C.L., and
Aggarwal, J.K.,
A Two-Stage Hybrid Approach to the Correspondence Problem Via
Forward-Searching and Backward-Correcting,
ICPR90(I: 173-179).
IEEE DOI Matching long sequences of point patterns using motion knowledge
(uncertainty inversely proportional to velocity).
BibRef
9000
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Principal Component Decompositions, Point features .