ARFBF Morphological Analysis

In the case of active phases observable in the form of stacked sheets, the classic strategy is to segment these sheets individually and characterize their length or curvature [Celse, 2008], which may related to actibity [Gandubert, 2006]. Since the images are quite noisy at this level of resolution, this type of approach has the disadvantage of requiring filtering steps that may deteriorate the measurement before performing the segmentation step. Moreover, this type of approach handles very poorly the cases of packets of intersecting sheets, which is quite frequent in practice. Our strategy has been to move towards an approach requiring no segmentation by working locally in the frequency domain. In Fourier space, packets of sheets form characteristic peaks whose shape can give information on the homogeneity of orientation and the homogeneity of spacing between sheets. However, this direct analysis is delicate and in order to implement it efficiently, we use stochastic models such as autoregressive [Haralick, 1979] or fractional Brownian [Pesquet, 2002] models that allow us to remove the frequency contribution of the media and reduce noise. This work [Tan, 2015] [Tan, 2016] was carried out within the framework of Zhangyun Tan's thesis, defended in December 2016, in collaboration with LHC (UMR CNRS 5516, Saint Etienne, France) and LISTIC (Univ. Savoie Mont Blanc, Annecy France).

This module is divided into several phases numbered from 1 to 4 and corresponds to the different steps detailed in [Tan, 2016].

First of all, it is imperative to install the Matlab R2015a 64bits runtime libraries version (MCR_R2015a_win64_installer) downloadable at the following address :

https://fr.mathworks.com/products/compiler/matlab-runtime.html

We provide with the link below some typical images to test the plugin.

If you use this plugin, cite :
Z. Tan, M. Moreaud, O. Alata, A.M. Atoo. ARFBF Morphological Analysis – Application to the Discrimination of Catalyst Active Phases. Image Analysis and Stereology 37(1), p. 21-34 (2018).

References :
[Celse, 2008] B. Celse, S. Bres, F. Moreau, P. Gueroult, et L. Sorbier. Semi-Automatic detection of sulfur slabs, STERMAT 2008 : VIII International Conference on Stereology and Image Analysis in Materials Sciences, 2–6 septembre 2008 Zakopane, Polska. Inzynieria Materiałowa, 29 (4), p. 421–426 (2008).
[Gandubert, 2006] A. Gandubert. Characterization and quantificatioin of the sulfided phase of hydrotreatment catalysts, Thèse, Université de Lille (2006).
[Haralick, 1979] R.M. Haralick. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), p.786 – 804 (1979).
[Pesquet, 2002] B. Pesquet-Popescu, J.L. Véhel. Stochastic fractal models for image processing. IEEE Signal Processing Magazine, 19, p. 48–62 (2002).
[Tan, 2015] Z. Tan, A.M. Atto, O. Alata, M. Moreaud. ARFBF model for non-stationary random fields and application in HRTEM images. Image Processing (ICIP), 2015 IEEE International Conference on, p. 2651-265 (2015).
[Tan, 2016] Z. Tan, M. Moreaud, O. Alata, A.M. Atoo. ARFBF Morphological Analysis – Application to the Discrimination of Catalyst Active Phases. Image Analysis and Stereology 37(1), p. 21-34 (2018).