Unsupervised and Accurate Extraction of Primitive Unit Cells from Crystal Images
| Autor: | Benjamin Berkels, Niklas Mevenkamp |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Cham : Springer International Publishing; Springer, Lecture Notes in Computer Science 9358, 105-116 (2015). doi:10.1007/978-3-319-24947-6_9 Pattern Recognition : 37th German Conference, GCPR 2015, Aachen, Germany, October 7–10, 2015, Proceedings / Gall, Juergen [Hrsg.] ; Gehler, Peter [Hrsg.] ; Leibe, Bastian [Hrsg.] Pattern Recognition : 37th German Conference, GCPR 2015, Aachen, Germany, October 7–10, 2015, Proceedings / Gall, Juergen [Hrsg.] ; Gehler, Peter [Hrsg.] ; Leibe, Bastian [Hrsg.]37. German Conference on Pattern Recognition, GCPR 2015, Aachen, Germany, 2015-10-07-2015-10-09 Lecture Notes in Computer Science ISBN: 9783319249469 GCPR |
| DOI: | 10.1007/978-3-319-24947-6_9 |
| Popis: | We present a novel method for the unsupervised estimation of a primitive unit cell, i.e. a unit cell that can’t be further simplified, from a crystal image. Significant peaks of the projective standard deviations of the image serve as candidate lattice vector angles. Corresponding fundamental periods are determined by clustering local minima of a periodicity energy. Robust unsupervised selection of the number of clusters is obtained from the likelihoods of multi-variance cluster models induced by the Akaike information criterion. Initial estimates for lattice angles and periods obtained in this manner are refined jointly using non-linear optimization. Results on both synthetic and experimental images show that the method is able to estimate complex primitive unit cells with sub-pixel accuracy, despite high levels of noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|