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Automatic recognition of hematite grains under polarized reflected light microscopy through image analysis

机译:在偏光反射显微镜下通过图像分析自动识别赤铁矿晶粒

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摘要

The recognition of hematite grains is an intermediate task that aids the texture characterization of iron ores. Hematite is a strongly anisotropic mineral. Thus, the combined use of a polarizer and an analyzer in reflected light microscopy (RLM) can be used to obtain images that present sufficient contrast to differentiate grains. The present work proposes a methodology for recognizing hematite grains in images obtained with RLM. Three images per field are acquired in different conditions: without polarization in common bright field arrangement; and with polarization under two symmetrical polarizer/analyzer angles. These images are automatically registered. Then, the hematite grains are recognized through a modified region growing segmentation method based on reflectance and textural information. An optimal value for the polarization angle was determined. The results are promising. The vast majority of grains was correctly recognized. The automatically segmented images were compared to edited versions in which all crystals were correctly discriminated. A statistical comparison of crystal size and shape showed no statistical differences, to within 99% confidence, between automatic and edited segmentation results.
机译:赤铁矿晶粒的识别是一项中间任务,有助于铁矿石的纹理表征。赤铁矿是一种强烈各向异性的矿物。因此,偏振光镜和检偏镜在反射光显微镜(RLM)中的组合使用可用于获得具有足够对比度以区分晶粒的图像。本工作提出了一种在RLM获得的图像中识别赤铁矿晶粒的方法。在不同条件下每场获取三幅图像:在普通的明场布置中没有极化;并且在两个对称的偏振器/检偏器角度下具有偏振。这些图像被自动注册。然后,通过基于反射率和纹理信息的改进区域生长分割方法识别赤铁矿晶粒。确定偏振角的最佳值。结果令人鼓舞。绝大多数谷物被正确识别。将自动分割的图像与已正确区分所有晶体的编辑版本进行比较。晶体大小和形状的统计比较显示,自动和编辑的分割结果之间没有统计学差异,置信度在99%以内。

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