RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Adjustable Invariant Features by Partial Haar-Integration
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Using irreducible group representations for invariant 3d shape description
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Voxel-wise gray scale invariants for simultaneous segmentation and classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Invariance in kernel methods by haar-integration kernels
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
SHREC'10 track: protein model classification
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
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The tremendous growth of 3D data models available on the Internet requires more skills for fast retrieval and classification algorithms. In particular, the problem of finding structural similarities between proteins automatically, in order to predict their functional similarity, is a challenging task. In this paper a new algebraic method for structural comparison between proteins based on invariant features computed by group integration with spherical harmonics and D-Wigner matrices is proposed. Our goal is to achieve good classification without alignment by using intrinsic, pose invariant features. We compare our method to DALI, PRIDE and the Gauss Integral method in a classification and search task. Additionally we provide a Web interface to test the proposed method.