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Classification of Crystal Shape Using Fourier Descriptors and Mathematical Morphology
Author(s) -
BernardMichel Bruno,
Rohani Sohrab,
Pons MarieNoelle,
Vivier Herve,
Hundal Harvinderjit S.
Publication year - 1997
Publication title -
particle and particle systems characterization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 56
eISSN - 1521-4117
pISSN - 0934-0866
DOI - 10.1002/ppsc.199700041
Subject(s) - fourier transform , linear discriminant analysis , crystallization , artificial neural network , optics , fourier analysis , factorial , function (biology) , crystal (programming language) , mathematics , pattern recognition (psychology) , fast fourier transform , artificial intelligence , image (mathematics) , discriminant , algorithm , computer science , physics , mathematical analysis , evolutionary biology , biology , thermodynamics , programming language
The performances of two image analysis methods for the classification of some randomly selected KCl crystals from a crystallization experiment into four two‐dimensional classes (nearly circular, square, rectangular and irregular) are compared. The first method uses the first 15 Fourier descriptors of the angular bend as a function of arc length of the periphery of the particles, whereas the second method is based on a combination of seven geometrical and morphological parameters of the crystals using a commercially available image analysis system (Visilog, Noesis, Orsay, France). The feedforward neural network with back‐propagation learning algorithm and discriminant factorial analysis (STATlab, SLP, Ivry sur Seine. France) were found to classify the crystals with similar success.