ARFBF morphological analysis - Application to the discrimination of catalyst active phases

Zhangyun Tan (1) , Maxime Moreaud (2) , Olivier Alata (3) , Abdourrahmane Atto (1)

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
2 IFPEN - IFP Energies nouvelles
3 LHC - Laboratoire Hubert Curien

This paper addresses the characterization of spatial arrangements of fringes in catalysts imaged by High Resolution Transmission Electron Microscopy (HRTEM). It presents a statistical model-based approach for analyzing these fringes. The proposed approach involves Fractional Brownian Field (FBF) and 2-D AutoRegressive (AR) modeling, as well as morphological analysis. The originality of the approach consists in identifying the image background as an FBF, subtracting this background, modeling the residual by 2-D AR so as to capture fringe information and, finally, discriminating catalysts from fringe characterizations obtained by morphological analysis. The overall analysis is called ARFBF (Auto-Regressive Fractional Brownian Field) based morphology characterization.

Mots clés: auto-regressive field; fractional Brownian field; mathematical morphology; HRTEM imaging; texture analysis

Domaines: Traitement du signal et de l'image [eess.SP]; Modélisation et simulation; Statistiques [math.ST]; Recherche d'information [cs.IR]

Zhangyun Tan, Maxime Moreaud, Olivier Alata, Abdourrahmane Atto. ARFBF morphological analysis - Application to the discrimination of catalyst active phases. Image Analysis & Stereology, 2018, 37 (1), pp.21-34. ⟨10.5566/ias.1624⟩. ⟨hal-01779425v2⟩