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Computer vision kernel approximations
This module provides kernel approximation methods specifically designed for computer vision tasks, including spatial pyramid features, texture kernels, convolutional features, and scale-invariant methods.
Structs§
- Convolutional
Kernel Features - Convolutional kernel features using random convolutions ConvolutionalKernelFeatures
- Fitted
Convolutional Kernel Features - FittedConvolutionalKernelFeatures
- Fitted
Scale Invariant Features - FittedScaleInvariantFeatures
- Fitted
Spatial Pyramid Features - FittedSpatialPyramidFeatures
- Fitted
Texture Kernel Approximation - FittedTextureKernelApproximation
- Scale
Invariant Features - Scale-invariant feature transform (SIFT-like) kernel approximation ScaleInvariantFeatures
- Spatial
Pyramid Features - Spatial pyramid kernel approximation for hierarchical spatial feature extraction SpatialPyramidFeatures
- Texture
Kernel Approximation - Texture kernel approximation using Local Binary Patterns and Gabor filters TextureKernelApproximation
Enums§
- Activation
Function - ActivationFunction
- Pooling
Method - PoolingMethod