Gradients and Laplacians
[Image processing]

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Detailed Description

• Gradient images derive from class qgar::AbstractGradientImage. They include two layers (pixel maps) for X and Y derivatives. They can be computed from grey-level or float images, using:

  • Canny's method (class qgar::CannyGradientImage)
  • Deriche's method (class qgar::DericheGradientImage)

  From a Gradient image, we can get a grey-level image representing:

  • the local maxima of the Gradient (class qgar::GradientLocalMaxImage)
  • the module of the Gradient (class qgar::GradientModuleImage)

• Laplacian of Gaussian images can be computed from grey-level or float images thanks to class qgar::LaplacianOfGaussianImage.

Classes
class	qgar::AbstractGradientImage
	Base class to define gradient images. More...
class	qgar::CannyGradientImage
	Canny Gradient image. More...
class	qgar::DericheGradientImage
	Deriche's optimal edge detector. More...
class	qgar::GradientLocalMaxImage
	Local maxima of the Gradient. More...
class	qgar::GradientModuleImage
	Module of the Gradient of an image. More...
class	qgar::LaplacianOfGaussianImage
	Laplacian of Gaussian image. More...