GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
PreProcessingModules Directory Reference

Files

file  DeadZone.cpp [code]
 
file  DeadZone.h [code]
 The DeadZone class sets any values in the input signal that fall within the dead-zone region to zero. Any values outside of the dead-zone region will be offset by the dead zone's lower limit and upper limit.
 
file  Derivative.cpp [code]
 
file  Derivative.h [code]
 The Derivative class computes either the first or second order derivative of the input signal.
 
file  DoubleMovingAverageFilter.cpp [code]
 
file  DoubleMovingAverageFilter.h [code]
 The class implements a Float moving average filter.
 
file  FIRFilter.cpp [code]
 
file  FIRFilter.h [code]
 This class implements a Finite Impulse Response (FIR) Filter.
 
file  HighPassFilter.cpp [code]
 
file  HighPassFilter.h [code]
 This class implements a High Pass Filter.
 
file  LeakyIntegrator.cpp [code]
 
file  LeakyIntegrator.h [code]
 The LeakyIntegrator class computes the following signal: y = y*z + x, where x is the input, y is the output and z is the leakrate.
 
file  LowPassFilter.cpp [code]
 
file  LowPassFilter.h [code]
 The class implements a low pass filter, this is based on an Exponential moving average filter: https://en.wikipedia.org/wiki/Exponential_smoothing.
 
file  MedianFilter.cpp [code]
 
file  MedianFilter.h [code]
 The MedianFilter implements a simple median filter.
 
file  MovingAverageFilter.cpp [code]
 
file  MovingAverageFilter.h [code]
 The MovingAverageFilter implements a low pass moving average filter.
 
file  SavitzkyGolayFilter.cpp [code]
 
file  SavitzkyGolayFilter.h [code]
 This implements a Savitzky-Golay filter. This code is based on the Savitzky Golay filter code from Numerical Recipes 3.
 
file  WeightedAverageFilter.cpp [code]
 
file  WeightedAverageFilter.h [code]
 The WeightedAverageFilter implements a weighted average filter that gives a larger weight to more recent samples, and a smaller weight to older samples.