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pcaflow.hpp File Reference
Implementation of the PCAFlow algorithm from the following paper: http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf. More...
Classes | |
class | cv::optflow::OpticalFlowPCAFlow |
PCAFlow algorithm. More... | |
class | cv::optflow::PCAPrior |
This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with "learn_prior.py" script beforehand. More... | |
Namespaces | |
namespace | cv |
"black box" representation of the file storage associated with a file on disk. | |
namespace | cv::optflow |
Functions | |
Ptr< DenseOpticalFlow > | cv::optflow::createOptFlow_PCAFlow () |
Creates an instance of PCAFlow. | |
Detailed Description
Implementation of the PCAFlow algorithm from the following paper: http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf.
[Wulff:CVPR:2015]
There are some key differences which distinguish this algorithm from the original PCAFlow (see paper):
- Discrete Cosine Transform basis is used instead of basis extracted with PCA. Reasoning: DCT basis has comparable performance and it doesn't require additional storage space. Also, this decision helps to avoid overloading the algorithm with a lot of external input.
- Usage of built-in OpenCV feature tracking instead of libviso.