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Adaptive Background Correction of Crystal Image Datasets: Towards Automated Process Control

机译：晶体图像数据集的自适应背景校正：迈向自动化过程控制

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摘要

Improving the data descriptor calculation of crystal’s physical properties requires sophisticated imaging techniques and algorithms. It has been possible to construct 2D population balance models benefiting from characteristic measurements of both crystal’s length and width, compared to the single representative sizes used in 1D models. Our aim is to ameliorate the procedure of determining shape (and not only size) factors, in an automated fashion and directly from the process, for implementation in future models. Here, approaches suitable for real-time applications were employed including engineered imaging sensors and adaptive algorithms. We described the latter in detail for varying 2D image datasets. Their basic concept is similar. Each is applicable to an entire dataset, thus demonstrating efficacy for a variety of particle environments. While the challenge of particle segmentation for higher concentrations was not scrutinized here, this approach reduced processing time, steps and supervision, for the benefit of certain applications requiring process monitoring and automation.
机译：改进晶体物理性质的数据描述符计算需要复杂的成像技术和算法。与1D模型中使用的单个代表尺寸相比，有可能构建晶体长度和宽度的特征测量的2D人口平衡模型。我们的目的是改善以自动时尚和直接从过程中以自动化方式确定形状（不仅规模）因素的程序，以便在未来的模型中实现。这里，采用适用于实时应用的方法，包括工程化成像传感器和自适应算法。我们详细描述了后者以进行改变的2D图像数据集。他们的基本概念是相似的。每个都适用于整个数据集，从而证明了各种颗粒环境的功效。虽然这里没有仔细审查粒子分割的粒子分割的挑战，但这种方法减少了处理时间，步骤和监督，以便有利于需要过程监测和自动化的某些应用。

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