通过预测晶体聚焦状态提升显微图像中结块分类精度
Instance camera focus prediction for crystal agglomeration classification
- 用实例聚焦预测网络量化镜头焦点，更贴近人眼观察
- 结合聚焦预测与分割模型，在两种晶体数据集上准确率更高
- 适合材料科学、工业质检中需要精准识别晶体结块的场景
结块指由于粒子间作用力导致晶体聚集的过程。从显微图像分析晶体结块极具挑战，源于二维成像的固有局限：重叠晶体可能在不同深度层却表现为连接状态。因光学显微镜景深浅，同一图像中聚焦与失焦的晶体通常位于不同深度层，不构成真实结块。为此，我们首先提出一种实例相机聚焦预测网络，预测2类聚焦等级，其结果比传统图像处理聚焦度量更符合视觉观察。随后将该聚焦预测与实例分割模型结合，用于结块分类。在高氯酸铵晶体和蔗糖晶体数据集上，本方法的结块分类与分割准确率均优于基线模型。
原文摘要 · Abstract (English)
Agglomeration refers to the process of crystal clustering due to interparticle forces. Crystal agglomeration analysis from microscopic images is challenging due to the inherent limitations of two-dimensional imaging. Overlapping crystals may appear connected even when located at different depth layers. Because optical microscopes have a shallow depth of field, crystals that are in-focus and out-of-focus in the same image typically reside on different depth layers and do not constitute true agglomeration. To address this, we first quantified camera focus with an instance camera focus prediction network to predict 2 class focus level that aligns better with visual observations than traditional image processing focus measures. Then an instance segmentation model is combined with the predicted focus level for agglomeration classification. Our proposed method has a higher agglomeration classification and segmentation accuracy than the baseline models on ammonium perchlorate crystal and sugar crystal dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。