CV_INTER_NN method.
void cvSmooth( const CvArr* src, CvArr* dst,
int smoothtype=CV_GAUSSIAN,
- int param1=3, int param2=0, double param3=0, double param4=0 );
+ int size1=3, int size2=0, double sigma1=0, double sigma2=0 );
param1×param2 neighborhood of the pixel.
+ size1×size2 neighborhood of the pixel.
If the neighborhood size varies from pixel to pixel, compute the sums
using integral image (cvIntegral).
-param1×param2 neighborhood of the pixel.
+size1×size2 neighborhood of the pixel.
param1×param2.
- param3 and param4 may optionally be
+ of aperture size size1×size2.
+ sigma1 and sigma2 may optionally be
used to specify shape of the kernel.
param1×param1.
+ of size size1×size1 (i.e. only square aperture can be used).
That is, for each pixel the result is the median computed
- over param1×param1 neighborhood.
+ over size1×size1 neighborhood.
param1 and
- space sigma=param2.
+ with color sigma=sigma1 and
+ spatial sigma=sigma2. If size1!=0, then
+ a circular kernel with diameter size1 is used;
+ otherwise the diameter of the kernel is computed from sigma2.
Information about bilateral filtering
can be found at
http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
param2 is zero, it is set to param1.
- When not 0, it should be odd too.
-size2 is zero, it is set to size1.
+ When not 0, it should be odd too.
+In case of Gaussian kernel this parameter may specify Gaussian sigma (standard deviation).
If it is zero, it is calculated from the kernel size:
sigma = (n/2 - 1)*0.3 + 0.8, where n=param1 for horizontal kernel,
@@ -840,20 +843,29 @@ void cvSmooth( const CvArr* src, CvArr* dst,
With the standard sigma for small kernels (3×3 to 7×7) the performance is better.
If param3 is not zero, while param1 and param2
- are zeros, the kernel size is calculated from the sigma (to provide accurate enough operation).
-param3) sigma in the vertical direction.
+ are zeros, the kernel size is calculated from the sigma (to provide accurate enough operation).
+
+ In case of Bilateral filter the parameter specifies color sigma; the larger the value, + the stronger the pasterization effect of the filter is.
+In case of non-square Gaussian kernel the parameter may be used to specify a different
+ (from param3) sigma in the vertical direction.
In case of Bilateral filter the parameter specifies spatial sigma; the larger the value,
+ the stronger the blurring effect of the filter. Note that with large sigma2
+ the processing speed decreases substantionally, so it is recommended to limit the kernel
+ size using the parameter size1.
-The function cvSmooth smoothes image using one of several methods. Every of the methods
-has some features and restrictions listed below
Blur with no scaling works with single-channel images only and supports accumulation of
+The function cvSmooth smoothes image using one of the pre-defined methods. Every of the methods
+has some features and restrictions listed below:
-Simple blur and Gaussian blur support 1- or 3-channel, 8-bit, 16-bit and 32-bit floating point images. -These two methods can process images in-place.
-Median and bilateral filters work with 1- or 3-channel 8-bit images and can not process images -in-place.
- +and accumulation of 32f to 32f format.Applies linear filter to image
@@ -3785,6 +3797,68 @@ the readiness flag for the corresponding image can be set in the next call (i.e. for all the image pairs except the very first oneCV_LKFLOW_PYR_A_READY is set).
+Constructs a tree of feature vectors
++CvFeatureTree* cvCreateFeatureTree(CvMat* desc); +
+The function cvCreateFeatureTree constructs a balanced kd-tree index of
+the given feature vectors. The lifetime of the desc matrix must exceed that
+of the returned tree. I.e., no copy is made of the vectors.
+
Destroys a tree of feature vectors
++void cvReleaseFeatureTree(CvFeatureTree* tr); +
+The function cvReleaseFeatureTree deallocates the given kd-tree.
+
Finds approximate k nearest neighbors of given vectors using best-bin-first search
++void cvFindFeatures(CvFeatureTree* tr, CvMat* desc, + CvMat* results, CvMat* dist, int k=2, int emax=20); +
+The function cvFindFeatures finds (with high probability) the k nearest
+neighbors in tr for each of the given (row-)vectors in desc, using
+best-bin-first searching ([Beis97]).
+The complexity of the entire operation is at most O(m*emax*log2(n)),
+where n is the number of vectors in the tree.
+
Orthogonal range search
++int cvFindFeaturesBoxed(CvFeatureTree* tr, + CvMat* bounds_min, CvMat* bounds_max, + CvMat* results); +
+The function cvFindFeaturesBoxed performs orthogonal range seaching on the
+given kd-tree. That is, it returns the set of vectors v in tr that satisfy
+bounds_min[i] <= v[i] <= bounds_max[i], 0 <= i < d, where d is the dimension
+of vectors in the tree.
+The function returns the number of such vectors found.
+
Finds intrinsic and extrinsic camera parameters using calibration pattern
++void cvCalibrationMatrixValues( const CvMat *calibMatr, + int imgWidth, int imgHeight, + double apertureWidth=0, double apertureHeight=0, + double *fovx=NULL, double *fovy=NULL, + double *focalLength=NULL, + CvPoint2D64f *principalPoint=NULL, + double *pixelAspectRatio=NULL ); +
+The function cvCalibrationMatrixValues computes various useful camera (sensor/lens)
+characteristics using the computed camera calibration matrix, image frame resolution in pixels
+and the physical aperture size.
Finds extrinsic camera parameters for particular view
@@ -5220,72 +5321,75 @@ to use multi-channelNx1or1xNarrays.
Nx1 or 1xN arrays.
Nx1 or 1xN arrays.