ARFBF形态分析-在催化剂活性相鉴别中的应用

IF 0.8 4区计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Image Analysis & Stereology Pub Date : 2018-04-12 DOI:10.5566/IAS.1624

Z. Tan, M. Moreaud, O. Alata, A. Atto

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引用次数: 0

摘要

利用高分辨率透射电子显微镜(HRTEM)对催化剂中条纹的空间排列进行了表征。提出了一种基于统计模型的方法来分析这些条纹。提出的方法包括分数布朗场(FBF)和二维自回归(AR)建模，以及形态分析。该方法的独创性在于将图像背景识别为FBF，减去该背景，通过二维AR建模残差以捕获条纹信息，最后通过形态学分析获得的条纹特征来区分催化剂。整体分析称为基于自回归分数布朗场(ARFBF)的形态学表征。

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

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.

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来源期刊

Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.