Overview
The optical image pipeline processes data in three stages:- Frame normalization: corrects for haze, uneven illumination, and low contrast
- Video segmentation: a deep learning model segments features frame-by-frame, maintaining temporal consistency across the video
- Metric computation: morphological properties are measured for each segmented feature per frame, then tracked as timeseries with statistical anomaly scoring
Key Metrics
| Metric | What It Tells You |
|---|---|
| Perimeter | Arc length of segmented feature boundaries, in pixels. |
| Circularity | How close a feature’s shape is to a perfect circle, computed as P²/(4πA). A value of 1.0 indicates a perfect circle. |
| Edge roughness | Deviation of the feature boundary from its convex hull, as a fraction of the convex hull perimeter. Higher values indicate more irregular edges. |
| Hausdorff distance | Maximum boundary displacement between consecutive frames, in pixels. Captures the largest local shape change between frames. |
- Guide
- Technical Details