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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.09004 |
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| _version_ | 1866917202881413120 |
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| author | Ji, Xiaoyu Zhang, Chenhao Downard, Tyler James Nagy, Zoltan Shakouri, Ali Zhu, Fengqing |
| author_facet | Ji, Xiaoyu Zhang, Chenhao Downard, Tyler James Nagy, Zoltan Shakouri, Ali Zhu, Fengqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09004 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Instance camera focus prediction for crystal agglomeration classification Ji, Xiaoyu Zhang, Chenhao Downard, Tyler James Nagy, Zoltan Shakouri, Ali Zhu, Fengqing Computer Vision and Pattern Recognition 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. |
| title | Instance camera focus prediction for crystal agglomeration classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.09004 |