
Generalized Computation of Gaussian Derivatives Using ITK
Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/1290 |
Published in The Insight Journal - 2007 July - December.
Submitted by Ivan Macia on 10-08-2007.
Computation of local image derivatives is an important operation in many image processing tasks that
involve feature detection and extraction, such as edges, corners or more complicated features. However,
derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Furthermore,
we present some useful classes and examples that make use of this new operator.
involve feature detection and extraction, such as edges, corners or more complicated features. However,
derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Furthermore,
we present some useful classes and examples that make use of this new operator.
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IJ_IMacia_Generalized_Computation_Gaussian_Derivatives_5.zip (327Kb)
IJ_IMacia_Generalized_Computation_Gaussian_Derivatives_5.tar.gz (293Kb)
license.txt (1000b)
IJ_IMacia_Generalized_Computation_Gaussian_Derivatives.pdf (274Kb) [view paper]
IJ_IMacia_Generalized_Computation_Gaussian_Derivatives_5.tar.gz (293Kb)
license.txt (1000b)
IJ_IMacia_Generalized_Computation_Gaussian_Derivatives.pdf (274Kb) [view paper]
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Keywords: | Gaussian derivatives, scale-space, kernel |
Toolkits: | ITK (moved into the sandbox), CMake |
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