Building and preprocessing of image data using indices of representativeness and classification applied to granular product characterization

• Published in: Journal of chemometrics Vol. 11; no. 6; pp. 469 - 482
• Main Authors: Ros, F., Guillaume, S., Bellon-Maurel, V., Bertrand, D.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-11-1997; Wiley
• Subjects: Chemistry; classification; Colloidal state and disperse state; Exact sciences and technology; General and physical chemistry; granular product histograms; image analysis; Physical and chemical studies. Granulometry. Electrokinetic phenomena; Grain size analysis; Image analysis; Particle size; Histogram; Statistical analysis; Investigation method; Granular material; Classification; Factorial design; Milling industry
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/(SICI)1099-128X(199711/12)11:6<469::AID-CEM491>3.0.CO;2-Q

The characterization of granular products using image analysis is complex, as defining sample size is a very difficult task (should one use weight or number of particles?) and because of the diversity of the data which can be extracted from the image. A three‐step procedure is applied: data extraction, data preprocessing and sample classification. We deal with the second step, once the image data have been extracted and gathered into histograms with a large number of intervals. The method we propose allows both the building of optimal size samples and the creation of data vectors appropriate for the third step. The originality of the method lies in the supervision of the data processing by taking into account the final goal, the discrimination into classes. Indices of stability and discrimination are created to build new histograms. To determine the optimal sample size, indices of representativeness and classification are used. This process has been tested on mill product images which are divided into three classes. The optimal sample size given by the representativeness index is 18 images, whereas it drops to 13 using the clasification index. For this example the features, if considered independently, are not informative enough to solve the problem (the best classification performance is 60%). It is necessary to develop a strategy where features are combined. This strategy is presented in a separate paper. © 1997 John Wiley & Sons, Ltd.