ComfyUI Custom Node — Phase Congruency Corner

A ComfyUI custom node that detects corners using Phase Congruency, an illumination-invariant and contrast-invariant feature detector based on the coherence of Fourier phase components across multiple scales and orientations (Kovesi, 1999).

What is Phase Congruency Corner Detection?

Classical corner detectors (Harris, Shi-Tomasi, FAST) rely on intensity gradients, so their output changes with lighting conditions and local contrast. Phase Congruency Corner Detection takes a fundamentally different approach:

A corner is detected at locations where Fourier components are maximally in phase across all orientations simultaneously — regardless of amplitude.

• Edges have high phase congruency in one dominant orientation.
• Corners have high phase congruency in every orientation at once.

This makes the detector:

• Illumination invariant — absolute brightness has no effect
• Contrast invariant — works equally on high-contrast and low-contrast regions
• Perceptually meaningful — closely matches human visual corner perception
• Theoretically grounded — based on Kovesi (1999) using log-Gabor filters

Node

Property	Value
Node name	Phase Congruency Corner
Category	image/filters
Input	IMAGE (RGB or grayscale, any resolution)
Output	IMAGE (grayscale corner map, white = strong corners)

Parameters

Parameter	Type	Default	Range	Description
image	IMAGE	—	—	Input image tensor
nscale	INT	4	2–8	Number of log-Gabor filter scales
norient	INT	6	2–8	Number of filter orientations
min_wavelength	INT	3	2–20	Minimum wavelength of finest-scale filter (pixels)
mult	FLOAT	2.1	1.5–4.0	Multiplicative factor between successive filter scales
sigma_on_f	FLOAT	0.55	0.1–1.0	Bandwidth of each log-Gabor filter (ratio σ/f₀)
k	FLOAT	2.0	0.5–10.0	Noise threshold in std devs above mean (Rayleigh)
cutoff	FLOAT	0.5	0.1–0.9	Butterworth low-pass filter cutoff frequency

Algorithm

Step 1 — Per-orientation Phase Congruency maps

For each of the norient orientations θ_i:

1. Build a log-Gabor bandpass filter at each of the nscale scales, multiplied by an angular spread function (raised cosine).
2. Apply each filter in the frequency domain (FFT) to obtain complex responses.
3. Sum complex responses across all scales → energy vector (E_real, E_imag).
4. Estimate noise threshold T via Rayleigh statistics on finest-scale amplitude: T = mean_noise + k × noise_std
5. Compute per-orientation phase congruency: PC_i = max(|energy| − T, 0) / (Σ amplitude + ε)

Step 2 — Structure matrix

Accumulate a symmetric 2×2 structure matrix at each pixel:

M = Σ_i  PC_i · [[cos²θ_i,    cosθ_i·sinθ_i],
                  [cosθ_i·sinθ_i,    sin²θ_i ]]

This is analogous to the Harris structure matrix, but uses phase congruency values instead of intensity gradients.

Step 3 — Corner response

The corner strength is the minimum eigenvalue of M:

λ_min = (Mxx + Myy) / 2 − sqrt(((Mxx − Myy) / 2)² + Mxy²)

A large λ_min means high phase congruency in every spatial direction, which is the geometric signature of a corner point.

Comparison: Edge vs. Corner

	Phase Congruency Edge	Phase Congruency Corner
Strong response when	PC is high in one direction	PC is high in all directions
Aggregation method	Mean of per-orientation PC maps	Min. eigenvalue of structure matrix
Typical output	Thin edge contours	Isolated corner blobs

Comparison with Other Corner Detectors

Method	Illumination Invariant	Contrast Invariant	Multi-scale
Harris	No	No	No
Shi-Tomasi	No	No	No
FAST	No	No	No
Phase Congruency Corner	Yes	Yes	Yes

Parameter Tuning Guide

Goal	Adjustment
Detect finer / more localised corners	Decrease min_wavelength
Detect coarser / broader junctions	Increase min_wavelength
More sensitive (at risk of noise)	Decrease k (e.g. 1.0)
Cleaner output, fewer false corners	Increase k (e.g. 4.0–6.0)
Better isotropy across all angles	Increase norient (e.g. 8)
Richer multi-scale information	Increase nscale (e.g. 6)
Reduce high-frequency ringing	Decrease cutoff (e.g. 0.4)

Installation

cd ComfyUI/custom_nodes
git clone https://github.com/bemoregt/ComfyUI_PCPoint.git

Restart ComfyUI. The node will appear under image/filters → Phase Congruency Corner.

Requirements

All dependencies are already present in a standard ComfyUI installation:

numpy
torch

Reference

Kovesi, P. (1999). Image Features from Phase Congruency. Videre: Journal of Computer Vision Research, 1(3).

License

MIT License — free for personal and commercial use.