13.7 General References for Matching

Chapter Contents (Back)
Matching, 3-D General. RANSAC.

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 Link. 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 DOI 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.
DOI Link Model-based recognition and learning. BibRef 9901
Earlier:
Applying VC-Dimension Analysis to Object Recognition,
ECCV94(A:237-250).
Springer DOI 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 DOI 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 DOI 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.
Elsevier DOI 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. 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.
Elsevier DOI 0509
BibRef

Nistér, D.[David],
Preemptive RANSAC for live structure and motion estimation,
MVA(16), No. 5, December 2005, pp. 321-329.
Springer DOI 0601
BibRef
Earlier: ICCV03(199-206).
IEEE DOI 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 Link. 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 0903
RANSAC; Robust estimation; Model fitting; Fundamental matrix estimation BibRef

Scherer-Negenborn, N.[Norbert], Schaefer, R.[Rolf],
Model Fitting with Sufficient Random Sample Coverage,
IJCV(89), No. 1, August 2010, pp. xx-yy.
Springer DOI 1004
RANSAC. Compute how many iterations should really be needed. BibRef

Toldo, R.[Roberto], Castellani, U.[Umberto], Fusiello, A.[Andrea],
The bag of words approach for retrieval and categorization of 3D objects,
VC(26), No. 10, October 2010, pp. 1257-1268.
WWW Link. 1101
BibRef
Earlier:
A Bag of Words Approach for 3D Object Categorization,
MIRAGE09(116-127).
Springer DOI 0905
BibRef
And:
Visual Vocabulary Signature For 3d Object Retrieval and Partial Matching,
3DOR09(21-28)
PDF File.
DOI Link 1301
BibRef

Toldo, R.[Roberto], Fusiello, A.[Andrea],
Real-time Incremental J-Linkage for Robust Multiple Structures Estimation,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef
Earlier:
Automatic Estimation of the Inlier Threshold in Robust Multiple Structures Fitting,
CIAP09(123-131).
Springer DOI 0909
BibRef
Earlier:
Robust Multiple Structures Estimation with J-Linkage,
ECCV08(I: 537-547).
Springer DOI 0810
Deal with multiple instances of the same structure, which complicate RANSAC operation. BibRef

Taylor, S.[Simon], Drummond, T.W.[Tom W.],
Binary Histogrammed Intensity Patches for Efficient and Robust Matching,
IJCV(94), No. 2, September 2011, pp. 241-265.
WWW Link. 1101
BibRef
Earlier:
Multiple Target Localisation at over 100 Fps,
BMVC09(xx-yy).
PDF File. 0909
Real-Time Systems. BibRef

Taylor, S.[Simon], Rosten, E.[Edward], Drummond, T.W.[Tom W.],
Robust feature matching in 2.3µs,
CVPRWS09(15-22).
IEEE DOI 0906
Real-Time Systems. BibRef

McIlroy, P.[Paul], Rosten, E.[Edward], Taylor, S.[Simon], Drummond, T.W.[Tom W.],
Deterministic Sample Consensus with Multiple Match Hypotheses,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Raguram, R.[Rahul], Chum, O.[Ondrej], Pollefeys, M.[Marc], Matas, J.[Jiri], Frahm, J.M.[Jan-Michael],
USAC: A Universal Framework for Random Sample Consensus,
PAMI(35), No. 8, 2013, pp. 2022-2038.
IEEE DOI 1307
RANSAC; robust estimation BibRef

Harrenstein, P., Manlove, D., Wooldridge, M.,
The Joy of Matching,
IEEE_Int_Sys(28), No. 2, 2013, pp. 81-85.
IEEE DOI 1307
Gale-Shapley algorithm for matching. BibRef

Tran, Q.H.[Quoc Huy], Chin, T.J.[Tat-Jun], Chojnacki, W.[Wojciech], Suter, D.[David],
Sampling Minimal Subsets with Large Spans for Robust Estimation,
IJCV(106), No. 1, January 2014, pp. 93-112.
Springer DOI 1402
robust parameter estimation,. BibRef

Hassner, T.[Tal], Assif, L.[Liav], Wolf, L.B.[Lior B.],
When standard RANSAC is not enough: Cross-media visual matching with hypothesis relevancy,
MVA(25), No. 4, May 2014, pp. 971-983.
WWW Link. 1404
BibRef

Imre, E.[Evren], Hilton, A.[Adrian],
Order Statistics of RANSAC and Their Practical Application,
IJCV(111), No. 3, February 2015, pp. 276-297.
WWW Link. 1503
BibRef

Wang, Y.[Yue], Zheng, J.[Jin], Xu, Q.Z.[Qi-Zhi], Li, B.[Bo], Hu, H.M.[Hai-Miao],
An improved RANSAC based on the scale variation homogeneity,
JVCIR(40, Part B), No. 1, 2016, pp. 751-764.
Elsevier DOI 1610
Scale variation homogeneity BibRef


Litman, R.[Roee], Korman, S.[Simon], Bronstein, A.[Alex], Avidan, S.[Shai],
Inverting RANSAC: Global model detection via inlier rate estimation,
CVPR15(5243-5251)
IEEE DOI 1510
BibRef

Fragoso, V.[Victor], Sen, P.[Pradeep], Rodriguez, S.[Sergio], Turk, M.[Matthew],
EVSAC: Accelerating Hypotheses Generation by Modeling Matching Scores with Extreme Value Theory,
ICCV13(2472-2479)
IEEE DOI 1403
extreme value theory; ransac; robust estimation BibRef

Lebeda, K.[Karel], Matas, J.G.[Jirķ G.], Chum, O.[Ondrej],
Fixing the Locally Optimized RANSAC,
BMVC12(95).
DOI Link 1301
BibRef

Khosla, A.[Aditya], Zhou, T.[Tinghui], Malisiewicz, T.[Tomasz], Efros, A.A.[Alexei A.], Torralba, A.[Antonio],
Undoing the Damage of Dataset Bias,
ECCV12(I: 158-171).
Springer DOI 1210
Program development ruined by particular datasets. BibRef

Distante, C.[Cosimo], Indiveri, G.[Giovanni],
RANSAC-LEL: An optimized version with least entropy like estimators,
ICIP11(1425-1428).
IEEE DOI 1201
BibRef

Monnin, D.[David], Bieber, E.[Etienne], Schmitt, G.[Gwenaél], Schneider, A.[Armin],
An Effective Rigidity Constraint for Improving RANSAC in Homography Estimation,
ACIVS10(II: 203-214).
Springer DOI 1012
BibRef

Meler, A.[Antoine], Decrouez, M.[Marion], Crowley, J.L.[James L.],
Betasac: A New Conditional Sampling for RANSAC,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Ni, K.[Kai], Jin, H.L.[Hai-Lin], Dellaert, F.[Frank],
GroupSAC: Efficient consensus in the presence of groupings,
ICCV09(2193-2200).
IEEE DOI 0909
BibRef

Enqvist, O.[Olof], Josephson, K.[Klas], Kahl, F.[Fredrik],
Optimal correspondences from pairwise constraints,
ICCV09(1295-1302).
IEEE DOI 0909
Removing errors using geometric constraints. BibRef

Sattler, T.[Torsten], Leibe, B.[Bastian], Kobbelt, L.[Leif],
SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter,
ICCV09(2090-2097).
IEEE DOI 0909
BibRef

Lara-Alvarez, C.[Carlos], Romero, L.[Leonardo], Flores, J.F.[Juan F.], Gomez, C.[Cuauhtemoc],
A Simple Sample Consensus Algorithm to Find Multiple Models,
CIARP09(918-925).
Springer DOI 0911
MuSAC alternative to RANSAC BibRef

Zhang, L.[Liang], Wang, D.[Demin],
LLN-based Model-Driven Validation of Data Points for Random Sample Consensus Methods,
ICPR10(3436-3439).
IEEE DOI 1008
BibRef

Zhang, L.[Liang], Rastgar, H.[Houman], Wang, D.[Demin], Vincent, A.[André],
Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences,
ISVC09(I: 447-456).
Springer DOI 0911
BibRef

He, Z.C.[Zhou-Can], Wang, Q.[Qing], Yang, H.[Heng],
TOCSAC: TOpology Constraint SAmple Consensus for Fast and Reliable Feature Correspondence,
ISVC09(II: 608-618).
Springer DOI 0911
BibRef

Choi, S.[Sunglok], Kim, T.[Taemin], Yu, W.[Wonpil],
Performance Evaluation of RANSAC Family,
BMVC09(xx-yy).
PDF File. 0909
Evaluation, RANSAC. RANSAC. BibRef

Choi, J.M.[Jong-Moo], Medioni, G.[Gerard],
StaRSaC: Stable random sample consensus for parameter estimation,
CVPR09(675-682).
IEEE DOI 0906
BibRef

Raguram, R.[Rahul], Frahm, J.M.[Jan-Michael],
RECON: Scale-adaptive robust estimation via Residual Consensus,
ICCV11(1299-1306).
IEEE DOI 1201
Robust method for noisy data. BibRef

Raguram, R.[Rahul], Frahm, J.M.[Jan-Michael], Pollefeys, M.[Marc],
Exploiting uncertainty in random sample consensus,
ICCV09(2074-2081).
IEEE DOI 0909
BibRef
Earlier:
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus,
ECCV08(II: 500-513).
Springer DOI 0810
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 0806
BibRef

Fan, L.X.[Li-Xin],
A Feature-Based Object Tracking Method Using Online Template Switching and Feature Adaptation,
ICIG11(707-713).
IEEE DOI 1109
BibRef

Fan, L.X.[Li-Xin], Pylvänäinen, T.[Timo],
Adaptive Sample Consensus for Efficient Random Optimization,
ISVC09(II: 252-263).
Springer DOI 0911
BibRef
And:
Efficient Random Sampling for Nonrigid Feature Matching,
ISVC09(I: 457-467).
Springer DOI 0911
BibRef
Earlier:
Robust Scale Estimation from Ensemble Inlier Sets for Random Sample Consensus Methods,
ECCV08(III: 182-195).
Springer DOI 0810
BibRef
Earlier: A2, A1:
Hill Climbing Algorithm for Random Sample Consensus Methods,
ISVC07(I: 672-681).
Springer DOI 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 0708
BibRef

Leung, A.P.[Alex Po], Gong, S.G.[Shao-Gang],
Optimizing Distribution-based Matching by Random Subsampling,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Zhang, W.[Wei], Kosecka, J.[Jana],
Generalized RANSAC Framework for Relaxed Correspondence Problems,
3DPVT06(854-860).
IEEE DOI 0606
BibRef

Rodehorst, V.[Volker], Hellwich, O.[Olaf],
Genetic Algorithm SAmple Consensus (GASAC): A Parallel Strategy for Robust Parameter Estimation,
RANSAC06(103).
IEEE DOI 0609
BibRef

Subbarao, R.[Raghav], Meer, P.[Peter],
Beyond RANSAC: User Independent Robust Regression,
RANSAC06(101).
IEEE DOI 0609
BibRef

Frahm, J.M.[Jan-Michael], Pollefeys, M.[Marc],
RANSAC for (Quasi-)Degenerate data (QDEGSAC),
CVPR06(I: 453-460).
IEEE DOI 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 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 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 Link. 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 DOI BibRef 9604
Earlier: CVPR93(220-225).
IEEE DOI BibRef
Earlier: A2, A1: MIT AI Memo-1373, July 1992.
WWW Link. 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 DOI 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 DOI 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 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, Text Matching .


Last update:Jan 22, 2017 at 18:17:25