Fischler, M.A., and
Bolles, R.C.,
Random Sample Consensus: A Paradigm for Model Fitting with
Applications to Image Analysis and Automated Cartography,
CACM(24), No. 6, June 1981, pp. 381-395.
BibRef
8106
And:
RCV87(726-740).
BibRef
Earlier:
DARPA80(71-88).
BibRef
And:
SRI-TN-213, March 1980.
WWW Version.
RANSAC.
Robust Technique.
BibRef
And:
A RANSAC-Based Approach to Model Fitting and Its Application to
Finding Cylinders in Range Data,
IJCAI81(637-643).
RANSAC algorithm for matching data points to the model. This
allows error points to be eliminated and thus ignored - find a
match that a majority of the points are happy with.
BibRef
Bolles, R.C.,
Robust Feature Matching Through Maximal Cliques,
SPIE(182), Imaging Applications for Automated Industrial Inspection
and Assembly, 1979, pp. 140-149.
BibRef
7900
Roth, G.[Gerhard], and
Levine, M.D.[Martin D.],
Minimal Subset Random Sampling for Pose Determination and Refinement,
AMV Strategies921992, pp. 1-21.
RANSAC.
See also Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. RANSAC is good and can be generalized and extended.
BibRef
9200
Suetens, P.,
Fua, P.V., and
Hanson, A.J.,
Some Computational Strategies for Object Recognition,
Surveys(24), No. 1, March 1992, pp. 5-62.
Survey, Matching.
Matching, Survey. Covers a number of different recognition techniques both from SRI
and many other locations. The survey is dated to about 1989.
BibRef
9203
Lindenbaum, M.,
Bounds on Shape-Recognition Performance,
PAMI(17), No. 7, July 1995, pp. 666-680.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Evaluation, Matching. Analysis of the shape matching task, no matter what the method, to determin
how good it can be.
BibRef
9507
Lindenbaum, M.[Michael],
Ben-David, S.[Shai],
VC-Dimension Analysis of Object Recognition Tasks,
JMIV(10), No. 1, January 1999, pp. 27-49.
WWW Version. Model-based recognition and learning.
BibRef
9901
Earlier:
Applying VC-Dimension Analysis to Object Recognition,
ECCV94(A:237-250).
Springer DOI Link
BibRef
And:
Applying VC-Dimension Analysis to 3D Object Recognition from
Perspective Projections,
AAAI-94(985-991).
BibRef
Shum, H.Y.,
Ikeuchi, K.,
Reddy, R.,
Principal Component Analysis with Missing Data and Its
Application to Polyhedral Object Modeling,
PAMI(17), No. 9, September 1995, pp. 854-867.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9509
And:
MfR01(Chapter I-1).
BibRef
Earlier:
Principal Component Analysis with Missing Data and Its
Application to Object Modeling,
CVPR94(560-565).
IEEE Abstract. IEEE Top Reference.
BibRef
And:
Virtual Reality Modeling from a Sequence of Range Images,
ARPA94(II:1189-1198).
BibRef
Liu, G.[Gang],
Haralick, R.M.[Robert M.],
Optimal matching problem in detection and recognition:
Performance Evaluation,
PR(35), No. 10, October 2002, pp. 2125-2139.
WWW Version.
0206
BibRef
Kay, S.M.,
Gabriel, J.R.,
An invariance property of the generalized likelihood ratio test,
SPLetters(10), No. 12, December 2003, pp. 352-355.
IEEE Abstract. IEEE Top Reference.
0401
Generalized likelihood ratio test (GLRT) is invariant with respect to
transformations for which the hypothesis testing problem itself is
invariant.
BibRef
Li, H.Z.[Hao-Zheng],
Liu, Z.Q.A.[Zhi-Qi-Ang],
Zhu, X.H.[Xiang-Hua],
Hidden Markov models with factored Gaussian mixtures densities,
PR(38), No. 11, November 2005, pp. 2022-2031.
WWW Version.
0509
BibRef
Nistér, D.[David],
Preemptive RANSAC for live structure and motion estimation,
MVA(16), No. 5, December 2005, pp. 321-329.
Springer DOI Link
0601
BibRef
Earlier:
ICCV03(199-206).
IEEE DOI Link
0311
BibRef
Kuhnert, M.[Matthias],
Voinov, A.[Alexey],
Seppelt, R.[Ralf],
Comparing Raster Map Comparison Algorithms for Spatial Modeling and
Analysis,
PhEngRS(71), No. 8, August 2005, pp. 975-984.
WWW Version.
0602
A review of existing algorithms to compare spatial patterns and
development of a new approach based on the expanding window approach.
BibRef
Cheng, C.M.[Chia-Ming],
Lai, S.H.[Shang-Hong],
A consensus sampling technique for fast and robust model fitting,
PR(42), No. 7, July 2009, pp. 1318-1329.
Elsevier DOI Link
WWW Version.
0903
RANSAC; Robust estimation; Model fitting; Fundamental matrix estimation
BibRef
Raguram, R.[Rahul],
Frahm, J.M.[Jan-Michael],
Pollefeys, M.[Marc],
A Comparative Analysis of RANSAC Techniques Leading to Adaptive
Real-Time Random Sample Consensus,
ECCV08(II: 500-513).
Springer DOI Link
0810
BibRef
Toldo, R.[Roberto],
Castellani, U.[Umberto],
Fusiello, A.[Andrea],
A Bag of Words Approach for 3D Object Categorization,
MIRAGE09(116-127).
Springer DOI Link
0905
BibRef
Toldo, R.[Roberto],
Fusiello, A.[Andrea],
Automatic Estimation of the Inlier Threshold in Robust Multiple
Structures Fitting,
CIAP09(123-131).
Springer DOI Link
0909
BibRef
Earlier:
Robust Multiple Structures Estimation with J-Linkage,
ECCV08(I: 537-547).
Springer DOI Link
0810
Deal with multiple instances of the same structure, which complicate RANSAC
operation.
BibRef
Marquez-Neila, P.[Pablo],
Miro, J.G.[Jacobo Garcia],
Buenaposada, J.M.[Jose M.],
Baumela, L.[Luis],
Improving RANSAC for fast landmark recognition,
VisLoc08(1-8).
IEEE DOI Link
0806
BibRef
Fan, L.X.[Li-Xin],
Pylvänäinen, T.[Timo],
Robust Scale Estimation from Ensemble Inlier Sets for Random Sample
Consensus Methods,
ECCV08(III: 182-195).
Springer DOI Link
0810
BibRef
Earlier: A2, A1:
Hill Climbing Algorithm for Random Sample Consensus Methods,
ISVC07(I: 672-681).
Springer DOI Link
0711
BibRef
Yao, B.[Benjamin],
Yang, X.[Xiong],
Zhu, S.C.[Song-Chun],
Introduction to a Large-Scale General Purpose Ground Truth Database:
Methodology, Annotation Tool and Benchmarks,
EMMCVPR07(169-183).
Springer DOI Link
0708
BibRef
Leung, A.P.[Alex Po],
Gong, S.G.[Shao-Gang],
Optimizing Distribution-based Matching by Random Subsampling,
CVPR07(1-8).
IEEE DOI Link
0706
BibRef
Zhang, W.[Wei],
Kosecka, J.[Jana],
Generalized RANSAC Framework for Relaxed Correspondence Problems,
3DPVT06(854-860).
IEEE DOI Link
0606
BibRef
Rodehorst, V.[Volker],
Hellwich, O.[Olaf],
Genetic Algorithm SAmple Consensus (GASAC):
A Parallel Strategy for Robust Parameter Estimation,
RANSAC06(103).
IEEE DOI Link
0609
BibRef
Subbarao, R.[Raghav],
Meer, P.[Peter],
Beyond RANSAC: User Independent Robust Regression,
RANSAC06(101).
IEEE DOI Link
0609
BibRef
Frahm, J.M.[Jan-Michael],
Pollefeys, M.[Marc],
RANSAC for (Quasi-)Degenerate data (QDEGSAC),
CVPR06(I: 453-460).
IEEE DOI Link
0606
BibRef
Capel, D.P.,
An Effective Bail-out Test for RANSAC Consensus Scoring,
BMVC05(xx-yy).
HTML Version.
0509
BibRef
Zuliani, M.,
Kenney, C.S.,
Manjunath, B.S.,
The Multiransac Algorithm and its Application to Detect Planar
Homographies,
ICIP05(III: 153-156).
IEEE DOI Link
0512
BibRef
Rozenfeld, S.[Stas],
Shimshoni, I.[Ilan],
The Modified pbM-Estimator Method and a Runtime Analysis Technique for
the RANSAC Family,
CVPR05(I: 1113-1120).
IEEE DOI Link
0507
See also Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography.
BibRef
Basri, R.,
On the Uniqueness of Correspondence under Orthographic
and Perspective Projections,
DARPA92(875-884).
BibRef
9200
And:
MIT AI Memo-1330, December 1991.
WWW Version. Epi-polar lines define the affine transformation.
BibRef
Weinshall, D., and
Basri, R.,
Distance Metric between 3D Models and 2D Images for
Recognition and Classification,
PAMI(18), No. 4, April 1996, pp. 465-479.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9604
Earlier:
CVPR93(220-225).
IEEE Abstract. IEEE Top Reference.
BibRef
Earlier: A2, A1:
MIT AI Memo-1373, July 1992.
WWW Version. Compute transformation based metrics that penalize the amount of
tranformation needed for the match. Optimal for affine
deformations.
BibRef
Ponce, J.,
Bajcsy, R.,
Metaxas, D.N.,
Binford, T.O.,
Forsyth, D.A.,
Hebert, M.,
Ikeuchi, K.,
Kak, A.C.,
Shapiro, L.G.,
Slaroff, S.,
Pentland, A.P., and
Stockman, G.C.,
Object Representation for Object Recognition,
CVPR94(147-152).
IEEE Abstract. IEEE Top Reference.
BibRef
9400
Panel DiscussionReport on the workshop panel.
BibRef
Yacoob, Y., and
Gold, Y.I.,
3D Object Recognition Via Simulated Particles Diffusion,
CVPR89(442-449).
IEEE Abstract. IEEE Top Reference.
Recognize Three-Dimensional Objects. Characterize shapes as a diffusion-like
process. Find the rotation and translation for the 3-D object.
BibRef
8900
Stockman, G.C.,
Object Recognition,
AIRI90(225-253).
BibRef
9000
Bhanu, B.[Bir], and
Burger, W.[Wilhelm],
Signal-to-Symbol Conversion for Structural Object Recognition
Using Hidden Markov Models,
ARPA94(II:1287-1291).
It seems to say the problem remains.
BibRef
9400
Gilmore, J.F.,
Pemberton, W.B.,
A Survey of Aircraft Classification Algorithms,
ICPR84(559-561).
BibRef
8400
Kanazawa, Y.S.[Yasu-Shi],
Kanatani, K.[Kenichi],
Do We Really Have to Consider Covariance Matrices for Image Features?,
ICCV01(II: 301-306).
IEEE DOI Link
0106
Issues in matching and use of the match results.
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
String Matching .