Mask Optimization for High-Precision Extraction of Geometric Features in Microscopic Scenes
Tianbo Kang, Jianpeng Zhang, Xin Zhao, Mingzhu Sun, Yunwang Zhang · 2026
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Description
Tianbo Kang and co-authors propose a mask refinement method for measuring tiny regular shapes under the microscope in a 2026 paper. Spheres, pores, and tubes in micro scenes are small, faint, and ragged edged, so ordinary segmentation outlines are too crude for precision measurement. Their framework starts with a segmentation pass boosted by adaptive super-resolution, then iterates, blending pixel evidence with the known geometry of the target to polish the outline toward a clean, continuous boundary.
On a sphere and tube assembly dataset, the method produced lower geometric errors on samples that could be fitted and raised the fitting success rate substantially. Removing either the dynamic super-resolution or the iterative refinement hurt performance, which the ablations read as proof both parts carry weight. The authors argue that baking geometric consistency into mask cleanup is the practical route to trustworthy micro measurement for assembly and quality control.
Catalog record
| CREATOR | Tianbo Kang, Jianpeng Zhang, Xin Zhao, Mingzhu Sun, Yunwang Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |