13.4.5 Object Recognition Evaluation

Chapter Contents (Back)
Evaluation, Recognition.

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 .


Last update:Aug 2, 2021 at 20:26:03