Ranade, S., and
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
Yada, S.[Shiro], and
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
Labeled Point Pattern Matching by Fuzzy Relaxation,
PR(17), No. 5, 1984, pp. 569-573.
Elsevier DOI BibRef 8400
Labeled Point Pattern Matching by Delaunay Triangulation and Maximal Cliques,
PR(19), No. 1, 1986, pp. 35-40.
Elsevier DOI BibRef 8600
Relaxation for Point-Pattern Matching: What it really Computes,
CVPR85(405-407). Univ. of Massachusetts. Preliminary. BibRef 8500
Rotation and Scale Change Invariant Point Pattern Relaxation Matching by the Hopfield Neural Network,
OptEng(36), No. 12, December 1997, pp. 3378-3385. 9801
Computer Tracking of Moving Point Targets in Space,
PAMI(3), No. 5, September 1981, pp. 606-611. BibRef 8109
Sethi, I.K., and
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
Using Motion from Orthographic Views to Verify 3-D Point Matches,
PAMI(13), No. 9, September 1991, pp. 872-878.
IEEE DOI BibRef 9109
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
Feature Point Correspondence in the Presence of Occlusion,
PAMI(12), No. 1, January 1990, pp. 87-91.
IEEE DOI BibRef 9001
Correspondence in Presence of Occlusion,
CVWS87(327-330). A modification of
See also Finding Trajectories of Feature Points in a Monocular Image Sequence. BibRef
Feature Point Matching in Image Sequences,
PRL(7), 1988, pp. 113-121. BibRef 8800
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
Local Association Based Recognition of Two-Dimensional Objects,
MVA(5), 1992, pp. 265-276. BibRef 9200
Tsakiris, D.P.[Dimitris P.],
On the Visual Mathematics of Tracking,
IVC(9), No. 4, August 1991, pp. 235-251.
Elsevier DOI BibRef 9108
Tracking in a complex visual environment,
Springer DOI 9004
Tracking known shape with rigid motion. BibRef
Finding Point Correspondences in Motion Sequences Preserving Affine Structure,
CVIU(68), No. 2, November 1997, pp. 237-246.
DOI Link 9712
PDF File. BibRef
Establishing Motion-Based Feature Point Correspondence,
PR(31), No. 1, January 1998, pp. 23-30.
Elsevier DOI 9802
Cheng, C.L., and
A Two-Stage Hybrid Approach to the Correspondence Problem Via Forward-Searching and Backward-Correcting,
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 .