| private cvFindFeatures ( IntPtr tr, IntPtr desc, IntPtr results, IntPtr dist, int k, int emax ) : void | ||
| tr | IntPtr | |
| desc | IntPtr | |
| results | IntPtr | |
| dist | IntPtr | |
| k | int | |
| emax | int | |
| Результат | void | |
/// <summary>
/// Finds (with high probability) the k nearest neighbors in tree 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
/// </summary>
/// <param name="descriptors">The m feature descriptors to be searched from the feature tree</param>
/// <param name="results">
/// The results of the best <paramref name="k"/> matched from the feature tree. A m x <paramref name="k"/> matrix. Contains -1 in some columns if fewer than k neighbors found.
/// For each row the k neareast neighbors are not sorted. To findout the closet neighbour, look at the output matrix <paramref name="dist"/>.
/// </param>
/// <param name="dist">
/// A m x <paramref name="k"/> matrix of the distances to k nearest neighbors
/// </param>
/// <param name="k">The number of neighbors to find</param>
/// <param name="emax">For k-d tree only: the maximum number of leaves to visit. Use 20 if not sure</param>
private void FindFeatures(float[][] descriptors, Matrix <Int32> results, Matrix <double> dist, int k, int emax)
{
using (Matrix <float> descriptorMatrix = Util.GetMatrixFromDescriptors(descriptors))
CvInvoke.cvFindFeatures(Ptr, descriptorMatrix.Ptr, results.Ptr, dist.Ptr, k, emax);
}
