Neural Classification
Classify surface pixels into two classes using a simple two-layer feedforward neural network with sigmoid activations. Features are extracted via multi-scale Gaussian filtering. Equivalent in purpose to Gwyddion's neural.c classifier.
Inputs
| Name |
Type |
Required |
Description |
| field |
DATA_FIELD |
Yes |
Input surface to classify |
| training_mask |
IMAGE |
No |
Training labels: 0 = class A, 255 = class B. When omitted the network uses unsupervised self-labelling |
Outputs
| Name |
Type |
Description |
| mask |
IMAGE |
Binary classification mask (0 or 255) |
| probability |
DATA_FIELD |
Per-pixel probability of belonging to class B (values in 0-1) |
Controls
| Name |
Type |
Default |
Description |
| n_gaussians |
INT |
4 |
Number of Gaussian blur scales for feature extraction (1-10). Each scale uses sigma = 2^i |
| n_hidden |
INT |
16 |
Number of neurons in the hidden layer (4-128) |
| train_steps |
INT |
200 |
Number of gradient descent iterations (10-5000) |
| seed |
INT |
42 |
Random seed for weight initialisation (0-999999) |
Notes
- Feature extraction applies Gaussian blur at multiple scales (sigma = 1, 2, 4, 8, ...) to capture both fine and coarse surface structure. Each feature is normalised to zero mean and unit variance before training.
- The network architecture is input -> hidden (sigmoid) -> output (sigmoid), trained with binary cross-entropy loss and standard backpropagation.
- When a training mask is provided, the network learns in supervised mode using all pixels (0 pixels as class A targets, 255 pixels as class B targets).
- Without a training mask, the node uses an unsupervised approach: the random initial weights produce an initial classification which is then refined by self-training for a small number of steps.
- Increasing n_hidden or train_steps improves capacity but slows computation. For most surfaces, the defaults work well.
- The probability output can be fed into a Threshold Mask node for adjustable post-classification thresholding.
