Eggert, D.W.,
Lorusso, A.,
Fisher, R.B.,
Estimating 3-D Rigid-Body Transformations:
A Comparison of Four Major Algorithms,
MVA(9), No. 5-6, 1997, pp. 272-290.
Springer DOI
9705
BibRef
Edinburgh
Pose Estimation.
Evaluation, Pose. Euclidean transform from matched points.
For an earlier conference version:
PS File.
BibRef
Lorusso, A.,
Eggert, D.W., and
Fisher, R.B.,
A Comparison of Four Algorithms for Estimating 3-D
Rigid Transformations,
BMVC95(xx).
PDF File.
9509
BibRef
And:
DAI-No. 765, July 1995.
BibRef
Earlier:
DAI-No. 737, March 1995.
BibRef
Edinburgh
BibRef
Madsen, C.B.,
A Comparative-Study of the Robustness of Two Pose Estimation Techniques,
MVA(9), No. 5-6, 1997, pp. 291-303.
Springer DOI
9705
BibRef
Earlier:
PERF96(XX-YY).
HTML Version.
Evaluation, Matching.
BibRef
Liu, Y.[Yong],
Madsen, C.B.[Claus B.],
Störring, M.[Moritz],
An Extended Perspective Three Points Problem,
SCIA03(75-82).
Springer DOI
0310
BibRef
Aggarwal, J.K.,
Ghosh, J.,
Nair, D., and
Taha, I.,
A Comparative Study of Three Paradigms for Object Recognition:
Bayesian Statistics, Neural Networks, and Expert Systems,
AIU96(241-262).
Bayes Nets.
Neural Networks.
Expert Systems.
Evaluation.
BibRef
9600
Nair, D.,
Mitiche, A.,
Aggarwal, J.K.,
On comparing the performance of object recognition systems,
ICIP95(II: 631-634).
IEEE DOI
9510
BibRef
Walker, R.[Robert],
Evaluating the Performance of Spatially Explicit Models,
PhEngRS(69), No. 11, November 2003, pp. 1271-1278.
Statistical approaches to evaluating the performance of spatially explicit models are described.
WWW Link.
0401
BibRef
Mikolajczyk, K.,
Schmid, C.,
A Performance Evaluation of Local Descriptors,
PAMI(27), No. 10, October 2005, pp. 1615-1630.
IEEE DOI
0509
BibRef
Earlier:
CVPR03(II: 257-263).
IEEE DOI
0307
Award, Longuet-Higgins.
SIFT descriptors do best.
See also Distinctive Image Features from Scale-Invariant Keypoints.
BibRef
Rigamonti, R.[Roberto],
Lepetit, V.[Vincent],
González, G.[Germán],
Türetken, E.[Engin],
Benmansour, F.[Fethallah],
Brown, M.A.[Matthew A.],
Fua, P.[Pascal],
On the relevance of sparsity for image classification,
CVIU(125), No. 1, 2014, pp. 115-127.
Elsevier DOI
1406
Sparse representations
BibRef
Rigamonti, R.[Roberto],
Brown, M.A.[Matthew A.],
Lepetit, V.[Vincent],
Are sparse representations really relevant for image classification?,
CVPR11(1545-1552).
IEEE DOI
1106
Conclusion: enforcing sparsity constraints actually does not
improve recognition performance.
BibRef
Kanwal, N.[Nadia],
Bostanci, E.[Erkan],
Clark, A.F.[Adrian F.],
Evaluation Method, Dataset Size or Dataset Content:
How to Evaluate Algorithms for Image Matching?,
JMIV(55), No. 3, July 2016, pp. 378-400.
Springer DOI
1604
What matters in evaluation.
BibRef
Ruiz-Lendínez, J.J.[Juan José],
Ariza-López, F.J.[Francisco Javier],
Ureńa-Cámara, M.A.[Manuel Antonio],
Expert Knowledge as Basis for Assessing an Automatic Matching
Procedure,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Shah, M.A.[Muhammad A.],
Olivier, R.[Raphael],
Raj, B.[Bhiksha],
Optimal Strategies For Comparing Covariates To Solve Matching
Problems,
ICPR21(10622-10628)
IEEE DOI
2105
Measurement, Machine learning, Probabilistic logic, Data models,
Task analysis, Probes, Optimal matching
BibRef
Fonaryov, M.[Mark],
Lindenbaum, M.[Michael],
On the Minimal Recognizable Image Patch,
ICPR21(6734-6741)
IEEE DOI
2105
Evaluation of accuracy with occlusions.
Image recognition, Tools, Character recognition, Task analysis,
Materials requirements planning
BibRef
Temel, D.,
Lee, J.,
Al Regib, G.,
Object Recognition Under Multifarious Conditions: A Reliability
Analysis and a Feature Similarity-Based Performance Estimation,
ICIP19(3033-3037)
IEEE DOI
1910
object dataset, controlled experiment with recognition platforms,
feature similarity
BibRef
Mukhaimar, A.[Ayman],
Tennakoon, R.[Ruwan],
Lai, C.Y.[Chow Yin],
Hoseinnezhad, R.[Reza],
Bab-Hadiashar, A.[Alireza],
Comparative Analysis of 3D Shape Recognition in the Presence of Data
Inaccuracies,
ICIP19(2471-2475)
IEEE DOI
1910
Shapes into meaningful categories.
3D classification, neural networks, point cloud classification,
robust 3D classification
BibRef
Molinari, D.[Dario],
Pasquale, G.[Giulia],
Natale, L.[Lorenzo],
Caputo, B.[Barbara],
Automatic Creation of Large Scale Object Databases from Web Resources:
A Case Study in Robot Vision,
CIAP19(II:488-498).
Springer DOI
1909
BibRef
Guo, Z.Y.[Zhen-Yu],
Wang, Z.J.[Z. Jane],
An Adaptive Descriptor Design for Object Recognition in the Wild,
ICCV13(2568-2575)
IEEE DOI
1403
domain adaptation; image descriptor; multiple kernel learning
BibRef
Vreeswijk, D.T.J.[Daan T.J.],
Snoek, C.G.M.[Cees G.M.],
van de Sande, K.E.A.[Koen E.A.],
Smeulders, A.W.M.[Arnold W.M.],
All vehicles are cars: subclass preferences in container concepts,
ICMR12(8).
DOI Link
1301
humans bias labeling images with a container label
BibRef
Barnard, K.[Kobus],
Yanai, K.[Keiji],
Johnson, M.[Matthew],
Gabbur, P.[Prasad],
Cross Modal Disambiguation,
CLOR06(238-257).
Springer DOI
0711
BibRef
Barnard, K.[Kobus],
Duygulu, P.[Pinar],
Guru, R.,
Gabbur, P.,
Forsyth, D.A.[David A.],
The effects of segmentation and feature choice in a translation model
of object recognition,
CVPR03(II: 675-682).
IEEE DOI
0307
BibRef
Böttger, T.[Tobias],
Ulrich, M.[Markus],
Steger, C.T.[Carsten T.],
Subpixel-Precise Tracking of Rigid Objects in Real-Time,
SCIA17(I: 54-65).
Springer DOI
1706
BibRef
Wiedemann, C.[Christian],
Ulrich, M.[Markus],
Steger, C.T.[Carsten T.],
Recognition and Tracking of 3D Objects,
DAGM08(xx-yy).
Springer DOI
0806
BibRef
Ulrich, M.[Markus],
Steger, C.T.[Carsten T.],
Performance Comparison of 2D Object Recognition Techniques,
PCV02(A: 368).
0305
BibRef
Ulrich, M.[Markus],
Steger, C.T.[Carsten T.],
Empirical Performance Evaluation of Object Recognition Methods,
EEMCV01(xx-yy).
0110
BibRef
Mundy, J.L., and
Heller, A.J.,
The Evolution and Testing of a Model-Based Object Recognition System,
ICCV90(268-282).
IEEE DOI
BibRef
9000
Heller, A.J., and
Mundy, J.L.,
Benchmark Evaluation of a Model-Based Object Recognition System,
DARPA90(727-741).
Matching, Evaluation.
Benchmarks.
BibRef
9000
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Object Recognition, Retrieval Datasets .