Krüger, N.[Norbert],
Peters, G.[Gabriele],
ORASSYLL: Object Recognition with Autonomously Learned and Sparse
Symbolic Representations Based on Metrically Organized
Local Line Detectors,
CVIU(77), No. 1, January 2000, pp. 48-77.
DOI Link
0001
BibRef
Krüger, N.,
Lüdtke, N.,
Orassyll: Object Recognition with Autonomously Learned and
Sparse Symbolic Representations Based on Local Line Detectors,
BMVC98(xx-yy).
BibRef
9800
Krüger, N.[Norbert],
Object Recognition with Representations Based on Sparsified Gabor
Wavelets used as Local Line Detectors,
CAIP99(225-233).
Springer DOI
9909
BibRef
Peters, G.[Gabriele],
A Vision System for Interactive Object Learning,
CVS06(32).
IEEE DOI
0602
BibRef
Sidorova, J.,
Anisimova, M.,
NLP-inspired structural pattern recognition in chemical application,
PRL(45), No. 1, 2014, pp. 11-16.
Elsevier DOI
1407
Structural pattern recognition
BibRef
Buch, A.G.,
Kiforenko, L.,
Kraft, D.,
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object
Recognition,
ICCV17(4137-4145)
IEEE DOI
1802
Hough transforms, data handling, feature extraction, group theory,
object recognition, pattern clustering, pose estimation, vectors,
BibRef
Katsoulas, D.[Dimitrios],
Localization of Piled Boxes by Means of the Hough Transform,
DAGM03(44-51).
Springer DOI
0310
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Line Based Matching for Pose Estimation .