Class cv::ximgproc::RICInterpolator#
Sparse match interpolation algorithm based on modified piecewise locally-weighted affine estimator called Robust Interpolation method of Correspondences or RIC from [148] and Variational and Fast Global Smoother as post-processing filter. The RICInterpolator is a extension of the EdgeAwareInterpolator. Main concept of this extension is an piece-wise affine model based on over-segmentation via SLIC superpixel estimation. The method contains an efficient propagation mechanism to estimate among the pieces-wise models.
Collaboration diagram for cv::ximgproc::RICInterpolator:
Public Member Functions#
Public Member Functions inherited from cv::ximgproc::SparseMatchInterpolator
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Interpolate input sparse matches. |
Public Member Functions inherited from cv::Algorithm
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Clears the algorithm state. |
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Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. |
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Reads algorithm parameters from a file storage. |
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Stores algorithm parameters in a file storage. |
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Static Public Member Functions#
Static Public Member Functions inherited from cv::Algorithm
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Loads algorithm from the file. |
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Loads algorithm from a String. |
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Reads algorithm from the file node. |
Additional Inherited Members#
Protected Member Functions inherited from cv::Algorithm
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Detailed Description#
Sparse match interpolation algorithm based on modified piecewise locally-weighted affine estimator called Robust Interpolation method of Correspondences or RIC from [148] and Variational and Fast Global Smoother as post-processing filter. The RICInterpolator is a extension of the EdgeAwareInterpolator. Main concept of this extension is an piece-wise affine model based on over-segmentation via SLIC superpixel estimation. The method contains an efficient propagation mechanism to estimate among the pieces-wise models.
Member Function Documentation#
getAlpha()#
float cv::ximgproc::RICInterpolator::getAlpha()
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
See also
getFGSLambda()#
float cv::ximgproc::RICInterpolator::getFGSLambda()
Sets the respective fastGlobalSmootherFilter() parameter.
See also
getFGSSigma()#
float cv::ximgproc::RICInterpolator::getFGSSigma()
Sets the respective fastGlobalSmootherFilter() parameter.
See also
getK()#
int cv::ximgproc::RICInterpolator::getK()
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of [148] uses 32.
See also
getMaxFlow()#
float cv::ximgproc::RICInterpolator::getMaxFlow()
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
See also
getModelIter()#
int cv::ximgproc::RICInterpolator::getModelIter()
Parameter defining the number of iterations for piece-wise affine model estimation.
See also
getRefineModels()#
bool cv::ximgproc::RICInterpolator::getRefineModels()
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
See also
getSuperpixelMode()#
int cv::ximgproc::RICInterpolator::getSuperpixelMode()
Parameter to choose superpixel algorithm variant to use:
See also
getSuperpixelNNCnt()#
int cv::ximgproc::RICInterpolator::getSuperpixelNNCnt()
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
See also
getSuperpixelRuler()#
float cv::ximgproc::RICInterpolator::getSuperpixelRuler()
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
See also
getSuperpixelSize()#
int cv::ximgproc::RICInterpolator::getSuperpixelSize()
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
See also
getUseGlobalSmootherFilter()#
bool cv::ximgproc::RICInterpolator::getUseGlobalSmootherFilter()
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
See also
getUseVariationalRefinement()#
bool cv::ximgproc::RICInterpolator::getUseVariationalRefinement()
Parameter to choose wether the VariationalRefinement post-processing is employed.
See also
setAlpha()#
void cv::ximgproc::RICInterpolator::setAlpha(float alpha = 0.7f)
Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
setCostMap()#
void cv::ximgproc::RICInterpolator::setCostMap(const Mat & costMap)
Interface to provide a more elaborated cost map, i.e. edge map, for the edge-aware term. This implementation is based on a rather simple gradient-based edge map estimation. To used more complex edge map estimator (e.g. StructuredEdgeDetection that has been used in the original publication) that may lead to improved accuracies, the internal edge map estimation can be bypassed here.
See also
Parameters
costMap— a type CV_32FC1 Mat is required.
setFGSLambda()#
void cv::ximgproc::RICInterpolator::setFGSLambda(float lambda = 500.f)
Sets the respective fastGlobalSmootherFilter() parameter.
setFGSSigma()#
void cv::ximgproc::RICInterpolator::setFGSSigma(float sigma = 1.5f)
Sets the respective fastGlobalSmootherFilter() parameter.
setK()#
void cv::ximgproc::RICInterpolator::setK(int k = 32)
K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of [148] uses 32.
setMaxFlow()#
void cv::ximgproc::RICInterpolator::setMaxFlow(float maxFlow = 250.f)
MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
setModelIter()#
void cv::ximgproc::RICInterpolator::setModelIter(int modelIter = 4)
Parameter defining the number of iterations for piece-wise affine model estimation.
setRefineModels()#
void cv::ximgproc::RICInterpolator::setRefineModels(bool refineModles = true)
Parameter to choose wether additional refinement of the piece-wise affine models is employed.
setSuperpixelMode()#
void cv::ximgproc::RICInterpolator::setSuperpixelMode(int mode = 100)
Parameter to choose superpixel algorithm variant to use:
cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100)
cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101)
cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).
setSuperpixelNNCnt()#
void cv::ximgproc::RICInterpolator::setSuperpixelNNCnt(int spNN = 150)
Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
setSuperpixelRuler()#
void cv::ximgproc::RICInterpolator::setSuperpixelRuler(float ruler = 15.f)
Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
See also
setSuperpixelSize()#
void cv::ximgproc::RICInterpolator::setSuperpixelSize(int spSize = 15)
Get the internal cost, i.e. edge map, used for estimating the edge-aware term.
See also
setUseGlobalSmootherFilter()#
void cv::ximgproc::RICInterpolator::setUseGlobalSmootherFilter(bool use_FGS = true)
Sets whether the fastGlobalSmootherFilter() post-processing is employed.
setUseVariationalRefinement()#
void cv::ximgproc::RICInterpolator::setUseVariationalRefinement(bool use_variational_refinement = false)
Parameter to choose wether the VariationalRefinement post-processing is employed.
Source file#
The documentation for this class was generated from the following file:
opencv2/ximgproc/sparse_match_interpolator.hpp