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Classification of silver halide microcrystals via K ‐NN clustering of their shape descriptors
Author(s) -
Kindratenko Volodymyr V.,
Treiger Boris A.,
van Espen Pierre J. M.
Publication year - 1997
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199703)11:2<131::aid-cem460>3.0.co;2-6
Subject(s) - pattern recognition (psychology) , cluster analysis , artificial intelligence , k nearest neighbors algorithm , classifier (uml) , computer science , halide , nearest neighbour , chemistry , inorganic chemistry
A method for the classification of tabular grain silver halide microcrystals according to their shape is presented. Various approaches of shape analysis and recognition and their applicability for the given problem are discussed. Shape descriptors obtained from Fourier power spectra are used to describe the shape of microcrystals. The classification of the shapes is based on nearest neighborhood algorithms. Results of the classification by four different algorithms are compared. The fuzzy four‐nearest‐neighbor classifier was found to be the most appropriate one. © 1997 John Wiley & Sons, Ltd.