Urahama, K.,
Furukawa, Y.,
Gradient descent learning of nearest neighbor classifiers with outlier
rejection,
PR(28), No. 5, May 1995, pp. 761-768.
Elsevier DOI
0401
BibRef
Black, M.J.,
Rangarajan, A.,
On The Unification of Line Processes, Outlier Rejection, and
Robust Statistics with Applications in Early Vision,
IJCV(19), No. 1, July 1996, pp. 57-91.
Springer DOI
PDF File.
9608
BibRef
Earlier:
The Outlier Process: Unifying Line Processes and Robust Statistics,
CVPR94(15-22).
IEEE DOI Applied to reconstruction of degraded images.
BibRef
Tax, D.M.J.[David M.J.],
Duin, R.P.W.[Robert P.W.],
Growing a multi-class classifier with a reject option,
PRL(29), No. 10, 15 July 2008, pp. 1565-1570.
Elsevier DOI
0711
Multi-class classification, Outlier detection, Rejection
See also Precision-recall operating characteristic (P-ROC) curves in imprecise environments.
BibRef
Miller, D.J.,
Browning, J.,
A mixture model and EM-based algorithm for class discovery, robust
classification, and outlier rejection in mixed labeled/unlabeled data
sets,
PAMI(25), No. 11, November 2003, pp. 1468-1483.
IEEE Abstract.
0311
Augment the training set with unlabeled examples, assumed to come from
a know class or a completely new class.
Robust analysis.
BibRef
Grinstead, B.[Brad],
Koschan, A.F.[Andreas F.],
Gribok, A.V.[Andrei V.],
Abidi, M.A.[Mongi A.],
Gorsich, D.[David],
Outlier rejection by oriented tracks to aid pose estimation from video,
PRL(27), No. 1, 1 January 2006, pp. 37-48.
Elsevier DOI
0512
BibRef
Condessa, F.[Filipe],
Bioucas-Dias, J.M.[José M.],
Kovacevic, J.[Jelena],
Performance measures for classification systems with rejection,
PR(63), No. 1, 2017, pp. 437-450.
Elsevier DOI
1612
Classification with rejection
BibRef
Hanczar, B.[Blaise],
Performance visualization spaces for classification with rejection
option,
PR(96), 2019, pp. 106984.
Elsevier DOI
1909
Classification with reject option, Classifier performances
BibRef
Chen, S.X.[Shun-Xing],
Zheng, L.X.[Lin-Xin],
Xiao, G.B.[Guo-Bao],
Zhong, Z.[Zhen],
Ma, J.Y.[Jia-Yi],
CSDA-Net: Seeking reliable correspondences by channel-Spatial
difference augment network,
PR(126), 2022, pp. 108539.
Elsevier DOI
2204
Feature matching, Deep learning, Outlier rejection, Attention mechanism
BibRef
Shi, Z.W.[Zi-Wei],
Xiao, G.B.[Guo-Bao],
Zheng, L.X.[Lin-Xin],
Ma, J.Y.[Jia-Yi],
Chen, R.Q.[Ri-Qing],
JRA-Net: Joint representation attention network for correspondence
learning,
PR(135), 2023, pp. 109180.
Elsevier DOI
2212
Correspondences, Joint representation, Attention mechanism,
Outlier rejection, Pose estimation
BibRef
Calli, E.[Erdi],
van Ginneken, B.[Bram],
Sogancioglu, E.[Ecem],
Murphy, K.[Keelin],
FRODO: An In-Depth Analysis of a System to Reject Outlier Samples
From a Trained Neural Network,
MedImg(42), No. 4, April 2023, pp. 971-981.
IEEE DOI
2304
Task analysis, Biomedical imaging, X-ray imaging, Measurement,
Training, Neural networks, Deep learning, Deep learning, statistics
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Boosting, AdaBoost Technique .