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CGAL 6.2 - Feature graph
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#include <CGAL/Feature_graph/AmbrosioTortorelli_on_image.h>
Class that evaluates the Ambriosio-Tortorelli normals and sharpness measure from an image.
Two functors can then be retrieved to access the estimations.
| Vector_3 | the type of the normal vector model of Kernel::Vector_3. |
NormalEstimator Types | |
| typedef Vector_3 | Normal_type |
| The type of the normal vector. | |
| typedef unspecified_type | Sharpness_functor |
| The type of the functor that allows to retrieve the sharpness values. | |
| typedef unspecified_type | Normal_functor |
| The type of the functor that allows to retrieve the normals. | |
Constructor | |
| template<typename Image , typename FT = Sharpness_functor::Sharpness_value_type> | |
| AmbrosioTortorelli_on_image (const Image &image, const FT &selection_threshold=FT(0.25)) | |
| evaluates the normal and sharpness values using the Ambrosio-Tortorelli energy optimization. | |
Functor Accessors | |
| Sharpness_functor | sharpness_functor () const |
| returns the functor that allows to retrieve the sharpness values. | |
| Normal_functor | normal_functor () const |
| returns the functor that allows to retrieve the normals. | |
| typedef unspecified_type CGAL::Feature_graph::AmbrosioTortorelli_on_image< Vector_3 >::Normal_functor |
The type of the functor that allows to retrieve the normals.
NormalEstimator | typedef unspecified_type CGAL::Feature_graph::AmbrosioTortorelli_on_image< Vector_3 >::Sharpness_functor |
The type of the functor that allows to retrieve the sharpness values.
SharpnessEstimator | CGAL::Feature_graph::AmbrosioTortorelli_on_image< Vector_3 >::AmbrosioTortorelli_on_image | ( | const Image & | image, |
| const FT & | selection_threshold = FT(0.25) |
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| ) |
evaluates the normal and sharpness values using the Ambrosio-Tortorelli energy optimization.
| Image | the image type model of FeatureImage_3 |
| FT | a model of RealEmbeddable |
| image | the image. |
| selection_threshold | a threshold on the sharpness value. Elements with a sharpness value lower than this threshold are considered flat and will be given a negative value. |