13.7 General References for Matching

Chapter Contents (Back)
Matching, 3-D General. See also RANSAC Matching Issues, Design, Evaluation.

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

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

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

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

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


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

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

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

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
RANSAC Matching Issues, Design, Evaluation .


Last update:Oct 15, 2018 at 09:19:25