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Automated classification of crystallization experiments using wavelets and statistical texture characterization techniques
Author(s) -
Watts D.,
Cowtan K.,
Wilson J.
Publication year - 2008
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s0021889807049308
Subject(s) - wavelet , pattern recognition (psychology) , wavelet transform , gaussian , artificial intelligence , probability distribution , mathematics , texture (cosmology) , crystallization , statistical parameter , characterization (materials science) , computer science , statistics , image (mathematics) , optics , physics , quantum mechanics , thermodynamics
A method is presented for the classification of protein crystallization images based on image decomposition using the wavelet transform. The distribution of wavelet coefficient values in each sub‐band image is modelled by a generalized Gaussian distribution to provide discriminatory variables. These statistical descriptors, together with second‐order statistics obtained from joint probability distributions, are used with learning vector quantization to classify protein crystallization images.