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Functions to Convolve Images and Signals |

1D and 2D filters, including separable and recursive convolution, and non-linear diffusion

#include <vigra/convolution.hxx>
Namespace: vigra

- Common Filters
  Short-hands for the most common 2D convolution filters
- Convolution filters for multi-dimensional arrays.
  Convolution filters for arbitrary dimensional arrays (MultiArray etc.)
- Resampling Convolution Filters
  Resampling convolution filters
- Two-dimensional convolution functions
  2D non-separable convolution, with and without ROI mask
- vigra::Kernel2D
  Generic 2-dimensional discrete convolution kernel
- One-dimensional and separable convolution functions
  1D convolution and separable filters in 2 dimensions
- vigra::Kernel1D
  Generic 1-dimensional discrete convolution kernel
- Recursive convolution functions
  Recursive filters (1st and 2nd order)
- Non-linear Diffusion and Total Variation
  Edge-preserving smoothing
- BorderTreatmentMode
  Choose between different border treatment modes
- Kernel Argument Object Factories
  Factory functions to create argument objects to simplify passing kernels

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)