Semi-automatic classification of cementitious materials using scanning electron microscope images

L. Drumetz; M. Dalla Mura; S. Meulenyzer; S. Lombard; J. Chanussot

doi:10.1117/12.2182762

30 April 2015 Semi-automatic classification of cementitious materials using scanning electron microscope images

L. Drumetz, M. Dalla Mura, S. Meulenyzer, S. Lombard, J. Chanussot

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 953403 (2015) https://doi.org/10.1117/12.2182762
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

A new interactive approach for segmentation and classification of cementitious materials using Scanning Electron Microscope images is presented in this paper. It is based on the denoising of the data with the Block Matching 3D (BM3D) algorithm, Binary Partition Tree (BPT) segmentation and Support Vector Machines (SVM) classification. The latter two operations are both performed in an interactive way. The BPT provides a hierarchical representation of the spatial regions of the data and, after an appropriate pruning, it yields a segmentation map which can be improved by the user. SVMs are used to obtain a classification map of the image with which the user can interact to get better results. The interactivity is twofold: it allows the user to get a better segmentation by exploring the BPT structure, and to help the classifier to better discriminate the classes. This is performed by improving the representativity of the training set, adding new pixels from the segmented regions to the training samples. This approach performs similarly or better than methods currently used in an industrial environment. The validation is performed on several cement samples, both qualitatively by visual examination and quantitatively by the comparison of experimental results with theoretical values.

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L. Drumetz, M. Dalla Mura, S. Meulenyzer, S. Lombard, and J. Chanussot "Semi-automatic classification of cementitious materials using scanning electron microscope images", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953403 (30 April 2015); https://doi.org/10.1117/12.2182762

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KEYWORDS

Image segmentation

Cements

Denoising

Scanning electron microscopy

X-rays

X-ray imaging

Electron microscopes

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