Principle:Kornia Kornia Edge Postprocessing
| Knowledge Sources | |
|---|---|
| Domains | Vision Image_Processing |
| Last Updated | 2026-02-09 15:00 GMT |
Overview
Technique of refining raw edge detection outputs through thresholding, normalization, and visualization for downstream use.
Description
Raw edge detection outputs require post-processing before use in downstream tasks. Magnitude thresholding converts continuous gradient maps to binary edges: pixels above a threshold are edges, below are background. Sigmoid normalization maps unbounded logits (from deep models like DexiNed) to the [0, 1] range. Multi-scale fusion combines edge maps from different scales or detectors. Visualization converts single-channel edge maps to displayable formats (RGB images, PIL Images).
These operations bridge edge detection output and application requirements.
Usage
Use after any edge detection method to convert raw outputs to the format required by downstream tasks (binary masks, normalized maps, visual displays).
Theoretical Basis
Binary thresholding converts continuous edge magnitudes to binary:
Sigmoid normalization maps unbounded logits to [0, 1]:
Hysteresis thresholding uses two thresholds for edge classification:
for each pixel x:
if magnitude(x) > T_high:
mark as strong edge
elif T_low < magnitude(x) < T_high:
if any neighbor is a strong edge:
mark as strong edge (connected weak edge)
else:
suppress
else:
suppress