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RLOFOpticalFlowParameter Properties

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The RLOFOpticalFlowParameter type exposes the following members.

Properties
  NameDescription
Public propertyCrossSegmentationThreshold
Color similarity threshold used by cross-based segmentation. Only used if supportRegionType is Cross. With the cross-bassed segmentation motion boundaries can be computed more accurately
Public propertyGlobalMotionRansacThreshold
To apply the global motion prior motion vectors will be computed on a regularly sampled which are the basis for Homography estimation using RANSAC. The reprojection threshold is based on n-th percentil (given by this value [0 ... 100]) of the motion vectors magnitude.
Public propertyLargeWinSize
Maximal window size of the support region. If supportRegionType is Fixed this gives the exact support region size. The speed of the RLOF is related to the applied win sizes. The smaller the window size the lower is the runtime, but the more sensitive to noise is the method.
Public propertyMaxIteration
Number of maximal iterations used for the iterative refinement. Lower values can reduce the runtime but also the accuracy.
Public propertyMaxLevel
Maximal number of pyramid level used. The large this value is the more likely it is to obtain accurate solutions for long-range motions. The runtime is linear related to this parameter
Public propertyMinEigenValue
Threshold for the minimal eigenvalue of the gradient matrix defines when to abort the iterative refinement.
Public propertyNormSigma0
parameter of the shrinked Hampel norm
Public propertyNormSigma1
parameter of the shrinked Hampel norm
Public propertyPtr
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Public propertySmallWinSize
Minimal window size of the support region. This parameter is only used if supportRegionType is Cross
Public propertySolver
Variable specifies the iterative refinement strategy
Public propertySupportRegion
Variable specifies the support region shape extraction or shrinking strategy
Public propertyUseGlobalMotionPrior
Use global motion prior initialisation. It allows to be more accurate for long-range motion. The computational complexity is slightly increased by enabling the global motion prior initialisation.
Public propertyUseIlluminationModel
Use the Gennert and Negahdaripour illumination model instead of the intensity brightness constraint.
Public propertyUseInitialFlow
Use next point list as initial values. A good initialization can improve the algorithm accuracy and reduce the runtime by a faster convergence of the iteration refinement
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