:param maxCorners:Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
:param qualityLevel:Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :ocv:func:`ocl::cornerMinEigenVal` ) or the Harris function response (see :ocv:func:`ocl::cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected.
:param minDistance:Minimum possible Euclidean distance between the returned corners.
:param blockSize:Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :ocv:func:`cornerEigenValsAndVecs` .
:param useHarrisDetector:Parameter indicating whether to use a Harris detector (see :ocv:func:`ocl::cornerHarris`) or :ocv:func:`ocl::cornerMinEigenVal`.
:param harrisK:Free parameter of the Harris detector.
:param corners:Output vector of detected corners (it will be one row matrix with CV_32FC2 type).
:param mask:Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected.
:param prevImg:First 8-bit input image (supports both grayscale and color images).
:param nextImg:Second input image of the same size and the same type as ``prevImg`` .
:param prevPts:Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type.
:param nextPts:Output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image. When ``useInitialFlow`` is true, the vector must have the same size as in the input.
:param status:Output status vector (CV_8UC1 type). Each element of the vector is set to 1 if the flow for the corresponding features has been found. Otherwise, it is set to 0.
:param err:Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
:param nextImg:Second input image of the same size and the same type as ``prevImg`` .
:param u:Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param v:Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
:param err:Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed.
:param frame0:First frame (32-bit floating point images, single channel).
:param frame1:Second frame. Must have the same type and size as ``frame0`` .
:param fu:Forward horizontal displacement.
:param fv:Forward vertical displacement.
:param bu:Backward horizontal displacement.
:param bv:Backward vertical displacement.
:param pos:New frame position.
:param newFrame:Output image.
:param buf:Temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat: occlusion masks for first frame, occlusion masks for second, interpolated forward horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow, interpolated backward vertical flow.
ocl::KalmanFilter
--------------------
..ocv:class:: ocl::KalmanFilter
Kalman filter class. ::
class CV_EXPORTS KalmanFilter
{
public:
KalmanFilter();
//! the full constructor taking the dimensionality of the state, of the measurement and of the control vector
KalmanFilter(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
//! re-initializes Kalman filter. The previous content is destroyed.
void init(int dynamParams, int measureParams, int controlParams=0, int type=CV_32F);
The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2001]_.
The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2004]_. ::
Threshold defining whether the component is significant enough to be included into the background model. ``cf=0.1 => TB=0.9`` is default. For ``alpha=0.001``, it means that the mode should exist for approximately 105 frames before it is considered foreground.
Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to ``Tg``). If it is not close to any component, a new component is generated. ``3 sigma => Tg=3*3=9`` is default. A smaller ``Tg`` value generates more components. A higher ``Tg`` value may result in a small number of components but they can grow too large.
Initial variance for the newly generated components. It affects the speed of adaptation. The parameter value is based on your estimate of the typical standard deviation from the images. OpenCV uses 15 as a reasonable value.
Complexity reduction parameter. This parameter defines the number of samples needed to accept to prove the component exists. ``CT=0.05`` is a default value for all the samples. By setting ``CT=0`` you get an algorithm very similar to the standard Stauffer&Grimson algorithm.
Shadow threshold. The shadow is detected if the pixel is a darker version of the background. ``Tau`` is a threshold defining how much darker the shadow can be. ``Tau= 0.5`` means that if a pixel is more than twice darker then it is not shadow. See [ShadowDetect2003]_.
