Point Cloud Library (PCL)
1.14.1-dev
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Classes | |
class | BoykovKolmogorov |
boost implementation of Boykov and Kolmogorov's maxflow algorithm doesn't support negative flows which makes it inappropriate for this context. More... | |
struct | Color |
Structure to save RGB colors into floats. More... | |
struct | Gaussian |
Gaussian structure. More... | |
class | GMM |
class | GaussianFitter |
Helper class that fits a single Gaussian to color samples. More... | |
Typedefs | |
using | Image = pcl::PointCloud< Color > |
An Image is a point cloud of Color. More... | |
Enumerations | |
enum | TrimapValue { TrimapUnknown = -1 , TrimapForeground , TrimapBackground } |
User supplied Trimap values. More... | |
enum | SegmentationValue { SegmentationForeground = 0 , SegmentationBackground } |
Grabcut derived hard segmentation values. More... | |
Functions | |
float | colorDistance (const Color &c1, const Color &c2) |
Compute squared distance between two colors. More... | |
PCL_EXPORTS void | buildGMMs (const Image &image, const Indices &indices, const std::vector< SegmentationValue > &hardSegmentation, std::vector< std::size_t > &components, GMM &background_GMM, GMM &foreground_GMM) |
Build the initial GMMs using the Orchard and Bouman color clustering algorithm. More... | |
PCL_EXPORTS void | learnGMMs (const Image &image, const Indices &indices, const std::vector< SegmentationValue > &hard_segmentation, std::vector< std::size_t > &components, GMM &background_GMM, GMM &foreground_GMM) |
Iteratively learn GMMs using GrabCut updating algorithm. More... | |
using pcl::segmentation::grabcut::Image = typedef pcl::PointCloud<Color> |
An Image is a point cloud of Color.
Definition at line 190 of file grabcut_segmentation.h.
Grabcut derived hard segmentation values.
Enumerator | |
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SegmentationForeground | |
SegmentationBackground |
Definition at line 201 of file grabcut_segmentation.h.
User supplied Trimap values.
Enumerator | |
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TrimapUnknown | |
TrimapForeground | |
TrimapBackground |
Definition at line 199 of file grabcut_segmentation.h.
PCL_EXPORTS void pcl::segmentation::grabcut::buildGMMs | ( | const Image & | image, |
const Indices & | indices, | ||
const std::vector< SegmentationValue > & | hardSegmentation, | ||
std::vector< std::size_t > & | components, | ||
GMM & | background_GMM, | ||
GMM & | foreground_GMM | ||
) |
Build the initial GMMs using the Orchard and Bouman color clustering algorithm.
Referenced by pcl::GrabCut< PointT >::fitGMMs().
Compute squared distance between two colors.
[in] | c1 | first color |
[in] | c2 | second color |
PCL_EXPORTS void pcl::segmentation::grabcut::learnGMMs | ( | const Image & | image, |
const Indices & | indices, | ||
const std::vector< SegmentationValue > & | hard_segmentation, | ||
std::vector< std::size_t > & | components, | ||
GMM & | background_GMM, | ||
GMM & | foreground_GMM | ||
) |
Iteratively learn GMMs using GrabCut updating algorithm.
Referenced by pcl::GrabCut< PointT >::refineOnce().