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using |
Ptr = shared_ptr< SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT > > |
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using |
ConstPtr = shared_ptr< const SUSANKeypoint< PointInT, PointOutT, NormalT, Intensity > > |
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using |
PointCloudIn = typename Keypoint< PointInT, PointOutT >::PointCloudIn
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using |
PointCloudOut = typename Keypoint< PointInT, PointOutT >::PointCloudOut
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using |
KdTree = typename Keypoint< PointInT, PointOutT >::KdTree
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using |
PointCloudInConstPtr = typename PointCloudIn::ConstPtr |
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using |
PointCloudN = pcl::PointCloud< NormalT > |
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using |
PointCloudNPtr = typename PointCloudN::Ptr
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using |
PointCloudNConstPtr = typename PointCloudN::ConstPtr
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using |
Ptr = shared_ptr< Keypoint< PointInT, PointOutT > > |
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using |
ConstPtr = shared_ptr< const Keypoint< PointInT, PointOutT > > |
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using |
BaseClass = PCLBase< PointInT > |
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using |
KdTree = pcl::search::Search< PointInT > |
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using |
KdTreePtr = typename KdTree::Ptr
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using |
PointCloudIn = pcl::PointCloud< PointInT > |
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using |
PointCloudInPtr = typename PointCloudIn::Ptr
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using |
PointCloudInConstPtr = typename PointCloudIn::ConstPtr
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using |
PointCloudOut = pcl::PointCloud< PointOutT > |
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using |
SearchMethod = std::function< int(pcl::index_t, double, pcl::Indices &, std::vector< float > &)> |
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using |
SearchMethodSurface = std::function< int(const PointCloudIn &cloud, pcl::index_t index, double, pcl::Indices &, std::vector< float > &)> |
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SUSANKeypoint (float radius=0.01f, float distance_threshold=0.001f, float angular_threshold=0.0001f, float intensity_threshold=7.0f) |
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Constructor. More...
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~SUSANKeypoint () |
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Empty destructor. More...
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void |
setRadius (float radius) |
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set the radius for normal estimation and non maxima supression. More...
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void |
setDistanceThreshold (float distance_threshold) |
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void |
setAngularThreshold (float angular_threshold) |
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set the angular_threshold value for detecting corners. More...
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void |
setIntensityThreshold (float intensity_threshold) |
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set the intensity_threshold value for detecting corners. More...
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void |
setNormals (const PointCloudNConstPtr &normals) |
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set normals if precalculated normals are available. More...
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void |
setSearchSurface (const PointCloudInConstPtr &cloud) override |
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Provide a pointer to the input dataset that we need to estimate features at every point for. More...
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void |
setNumberOfThreads (unsigned int nr_threads) |
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Initialize the scheduler and set the number of threads to use. More...
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void |
setNonMaxSupression (bool nonmax) |
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Apply non maxima suppression to the responses to keep strongest corners. More...
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void |
setGeometricValidation (bool validate) |
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Filetr false positive using geometric criteria. More...
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Keypoint () |
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Keypoint () |
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Empty constructor. More...
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void |
harrisCorner (PointInT &output, PointInT &input, const float sigma_d, const float sigma_i, const float alpha, const float thresh) |
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void |
hessianBlob (PointInT &output, PointInT &input, const float sigma, bool SCALE) |
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void |
hessianBlob (PointInT &output, PointInT &input, const float start_scale, const float scaling_factor, const int num_scales) |
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void |
imageElementMultiply (PointInT &output, PointInT &input1, PointInT &input2) |
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~Keypoint () |
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Empty destructor. More...
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PointCloudInConstPtr |
getSearchSurface () |
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Get a pointer to the surface point cloud dataset. More...
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void |
setSearchMethod (const KdTreePtr &tree) |
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Provide a pointer to the search object. More...
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KdTreePtr |
getSearchMethod () |
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Get a pointer to the search method used. More...
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double |
getSearchParameter () |
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Get the internal search parameter. More...
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void |
setKSearch (int k) |
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Set the number of k nearest neighbors to use for the feature estimation. More...
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int |
getKSearch () |
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get the number of k nearest neighbors used for the feature estimation. More...
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void |
setRadiusSearch (double radius) |
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Set the sphere radius that is to be used for determining the nearest neighbors used for the key point detection. More...
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double |
getRadiusSearch () |
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Get the sphere radius used for determining the neighbors. More...
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pcl::PointIndicesConstPtr |
getKeypointsIndices () |
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void |
compute (PointCloudOut &output) |
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Base method for key point detection for all points given in <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod () More...
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int |
searchForNeighbors (pcl::index_t index, double parameter, pcl::Indices &indices, std::vector< float > &distances) const |
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Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface from setSearchSurface. More...
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template<typename PointInT, typename PointOutT, typename NormalT = pcl::Normal, typename IntensityT = pcl::common::IntensityFieldAccessor<PointInT>>
class pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >
SUSANKeypoint implements a RGB-D extension of the SUSAN detector including normal directions variation in top of intensity variation.
It is different from Harris in that it exploits normals directly so it is faster.
Original paper "SUSAN — A New Approach to Low Level Image Processing", Smith, Stephen M. and Brady, J. Michael
- Author
- Nizar Sallem
Definition at line 56 of file susan.h.
template<typename PointInT , typename PointOutT , typename NormalT = pcl::Normal, typename IntensityT = pcl::common::IntensityFieldAccessor<PointInT>>
pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >::SUSANKeypoint | ( | float |
radius = 0.01f , | | | float |
distance_threshold = 0.001f , | | | float |
angular_threshold = 0.0001f , | | | float |
intensity_threshold = 7.0f | | ) | |
| | inline |
template<typename PointInT , typename PointOutT , typename NormalT , typename IntensityT >
bool pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >::isWithinNucleusCentroid | ( | const Eigen::Vector3f & |
nucleus, | | | const Eigen::Vector3f & |
centroid, | | | const Eigen::Vector3f & |
nc, | | | const PointInT & |
point | | ) | |
const | | protected |
return true if a point lies within the line between the nucleus and the centroid
- Parameters
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[in] |
nucleus |
coordinate of the nucleus |
[in] |
centroid |
of the SUSAN |
[in] |
nc |
to centroid vector (used to speed up since it is constant for a given neighborhood) |
[in] |
point |
the query point to test against |
- Returns
- true if the point lies within [nucleus centroid]
Definition at line 256 of file susan.hpp.
template<typename PointInT , typename PointOutT , typename NormalT , typename IntensityT >
set the angular_threshold value for detecting corners.
Normals are considered as parallel if 1 - angular_threshold <= (Ni.Nj) <= 1
- Parameters
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Definition at line 76 of file susan.hpp.
template<typename PointInT , typename PointOutT , typename NormalT , typename IntensityT >
Filetr false positive using geometric criteria.
The nucleus and the centroid should at least distance_threshold_ from each other AND all the points belonging to the USAN must be within the segment [nucleus centroid].
- Parameters
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Definition at line 55 of file susan.hpp.