High throughput object recognition and sizing in disperse systems using an image processing tool based on template matching,Chemical Engineering & Technology

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High throughput object recognition and sizing in disperse systems using an image processing tool based on template matching
Chemical Engineering & Technology ( IF 1.8 ) Pub Date : 2020-07-07 , DOI: 10.1002/ceat.201900494
Annika Ricarda Völp ₁ , Felix Fessler ₁ , Jasmin Reiner ₂ , Norbert Willenbacher ₁

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Size and shape of dispersed objects defines properties of suspensions, emulsions, and foams, such as stability, texture, and flow. Accordingly, a rational product design requires reliable size distribution analysis. This is particularly challenging in dense foams. An endoscopic setup was optimized for bubble imaging minimizing light reflections, uneven illumination, and foam distortion. A software tool was developed detecting large quantities of foam bubbles at dispersed phase fractions up to 93 % from images with spatially varying contrast within minutes based on the template matching algorithm. Reliability of the method is also illustrated for a bimodal glass bead mixture, anisotropic nanocrystals, and emulsion droplets during freezing.

中文翻译：

使用基于模板匹配的图像处理工具在分散系统中进行高吞吐量对象识别和尺寸调整

分散物体的大小和形状决定了悬浮液、乳液和泡沫的特性，例如稳定性、质地和流动性。因此，合理的产品设计需要可靠的粒度分布分析。这在致密泡沫中尤其具有挑战性。内窥镜设置针对气泡成像进行了优化，最大限度地减少了光反射、照明不均匀和泡沫变形。开发了一种软件工具，基于模板匹配算法，在几分钟内从具有空间变化对比度的图像中检测到大量分散相分数高达 93% 的泡沫气泡。该方法的可靠性还说明了双峰玻璃珠混合物、各向异性纳米晶体和冷冻过程中的乳液液滴。

更新日期：2020-07-07

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