| | Member | Description |
|---|
  | BM(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Size, Size, Size, Boolean, Image<(Of <(Gray, Single>)>), Image<(Of <(Gray, Single>)>)) |
Calculates optical flow for overlapped blocks block_size.width * block_size.height pixels each, thus the velocity fields are smaller than the original images.
For every block in prev the functions tries to find a similar block in curr in some neighborhood of the original block or shifted by (velx(x0,y0),vely(x0,y0)) block as has been calculated by previous function call (if use_previous)
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| Farneback(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Single>)>), Image<(Of <(Gray, Single>)>), Double, Int32, Int32, Int32, Int32, Double, OPTICALFLOW_FARNEBACK_FLAG) |
Computes dense optical flow using Gunnar Farneback's algorithm
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| HS(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Boolean, Image<(Of <(Gray, Single>)>), Image<(Of <(Gray, Single>)>), Double, MCvTermCriteria) |
Computes flow for every pixel of the first input image using Horn & Schunck algorithm
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| LK(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Size, Image<(Of <(Gray, Single>)>), Image<(Of <(Gray, Single>)>)) |
Computes flow for every pixel of the first input image using Lucas & Kanade algorithm
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| PyrLK(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), array<PointF>[]()[], Size, Int32, MCvTermCriteria, LKFLOW_TYPE, array<PointF>[]()[]%, array<Byte>[]()[]%, array<Single>[]()[]%) |
Calculates optical flow for a sparse feature set using iterative Lucas-Kanade method in pyramids
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| PyrLK(Image<(Of <(Gray, Byte>)>), Image<(Of <(Gray, Byte>)>), array<PointF>[]()[], Size, Int32, MCvTermCriteria, array<PointF>[]()[]%, array<Byte>[]()[]%, array<Single>[]()[]%) |
Calculates optical flow for a sparse feature set using iterative Lucas-Kanade method in pyramids
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Methods
Public
| 
C#