Pixel-Vernier: A General Image-Based Approach For Particle Size Distribution Estimation

Gamal H. Seedahmed, Andy L. Ward
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Abstract

Particle size distribution estimation (PSDE) is a fundamental task for heterogeneous materials characterization and modeling. This paper presents a general image-based approach for PSDE, which is called collectively “Pixel-Vernier” or PV for short. Meaningful image segmentation is the main problem to be solved for image-based PSDE. To this end, the proposed approach combines markers-controlled watershed segmentation with a clustering algorithm to solve the delineation of the boundaries of the particles. The combined approach is embedded in a coarse-to-fine strategy using a one training parameter to adapt the algorithm to the underlying distributions of the particle size. This training parameter is restricted to the size of an averaging filter. PV decomposes the image into separate particle regions. The final results of these regions are used to compute several geometric attributes for particles such as the semi-major axis, the semi-minor axis, and the equivalent diameter. Then, the geometric attributes of all the particles are used to estimate size distribution and relevant statistics. PV can be used in a laboratory as well as in a field setting. It is tested successfully on a diverse set of images that represent materials like soils, texture, rocks, and Mars surface geology.
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像素游标:一种通用的基于图像的粒度分布估计方法
粒径分布估计(PSDE)是非均质材料表征和建模的基本任务。本文提出了一种通用的基于图像的PSDE方法,它被统称为“像素游标”或简称PV。有意义的图像分割是基于图像的PSDE需要解决的主要问题。为此,该方法将标记控制分水岭分割与聚类算法相结合,解决了粒子边界的划定问题。该组合方法嵌入到一个由粗到细的策略中,使用一个训练参数使算法适应粒度的潜在分布。这个训练参数受限于平均滤波器的大小。PV将图像分解为单独的粒子区域。这些区域的最终结果用于计算粒子的半长轴、半短轴和等效直径等几何属性。然后,利用所有颗粒的几何属性来估计颗粒的大小分布和相关统计。PV既可以在实验室使用,也可以在现场使用。它在一系列不同的图像上进行了成功的测试,这些图像代表了土壤、纹理、岩石和火星表面地质等材料。
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