| Tool Name | Artificial Neural Network Classification |
| Tool ID | 11 |
| Library ID | imagery_opencv |
| Version | 1.0 |
| Author(s) | O.Conrad (c) 2016, L.Piras (c) 2013 |
OpenCV - Open Source Computer Vision
OpenCV - Machine Learning Overview
| Name | Type | Identifier | Description | Constraints |
|---|---|---|---|---|
| Input | ||||
| Features | grid list, input | FEATURES | ||
| Training Samples | table, input | TRAIN_SAMPLES | Provide a class identifier in the first field followed by sample data corresponding to the input feature grids. | |
| Training Areas | shapes, input | TRAIN_AREAS | ||
| Output | ||||
| Classification | grid, output | CLASSES | ||
| Look-up Table | table, output, optional | CLASSES_LUT | A reference list of the grid values that have been assigned to the training classes. | |
| Options | ||||
| Normalize | boolean | NORMALIZE | Default: 0 | |
| Training | choice | MODEL_TRAIN | Available Choices: [0] training areas [1] training samples [2] load from file Default: 0 | |
| Class Identifier | table field | TRAIN_CLASS | ||
| Buffer Size | floating point number | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000 Default: 1.000000 |
| Load Model | file path | MODEL_LOAD | Use a model previously stored to file. | |
| Save Model | file path | MODEL_SAVE | Stores model to file to be used for subsequent classifications instead of training areas. | |
| Number of Layers | integer number | ANN_LAYERS | You can specify the number of layers in the network (not including input and output layer). | Minimum: 1 Default: 3 |
| Number of Neurons | integer number | ANN_NEURONS | You can specify the number of neurons in each layer of the network. | Minimum: 1 Default: 5 |
| Maximum Number of Iterations | integer number | ANN_MAXITER | Minimum: 1 Default: 300 | |
| Error Change (Epsilon) | floating point number | ANN_EPSILON | Termination criteria of the training algorithm. You can specify how much the error could change between the iterations to make the algorithm continue (epsilon). | Minimum: 0.000000 Default: 0.000000 |
| Activation Function | choice | ANN_ACTIVATION | Available Choices: [0] Identity [1] Sigmoid [2] Gaussian Default: 1 | |
| Function's Alpha | floating point number | ANN_ACT_ALPHA | Default: 1.000000 | |
| Function's Beta | floating point number | ANN_ACT_BETA | Default: 1.000000 | |
| Training Method | choice | ANN_PROPAGATION | Available Choices: [0] resilient propagation [1] back propagation Default: 1 | |
| Initial Update Value | floating point number | ANN_RP_DW0 | Default: 0.000000 | |
| Increase Factor | floating point number | ANN_RP_DW_PLUS | Minimum: 1.010000 Default: 1.200000 | |
| Decrease Factor | floating point number | ANN_RP_DW_MINUS | Minimum: 0.010000 Maximum: 0.990000 Default: 0.500000 | |
| Lower Value Update Limit | floating point number | ANN_RP_DW_MIN | Minimum: 0.010000 Default: 0.100000 | |
| Upper Value Update Limit | floating point number | ANN_RP_DW_MAX | Minimum: 1.010000 Default: 1.100000 | |
| Weight Gradient Term | floating point number | ANN_BP_DW | Default: 0.100000 | |
| Moment Term | floating point number | ANN_BP_MOMENT | Default: 0.100000 | |