Class SURF

Class for extracting Speeded Up Robust Features from an image.

public class SURF : Feature2D, IDisposable

Inherited Members: Feature2D.DescriptorSize

Feature2D.DescriptorType

Feature2D.DefaultNorm

Feature2D.Empty()

Feature2D.Detect(Mat, Mat)

Feature2D.Detect(InputArray, InputArray)

Feature2D.Detect(IEnumerable<Mat>, IEnumerable<Mat>)

Feature2D.Compute(InputArray, ref KeyPoint[], OutputArray)

Feature2D.Compute(IEnumerable<Mat>, ref KeyPoint[][], IEnumerable<Mat>)

Feature2D.DetectAndCompute(InputArray, InputArray, out KeyPoint[], OutputArray, bool)

Feature2D.Write(string)

Feature2D.Read(string)

Feature2D.GetDefaultName()

Algorithm.Write(FileStorage)

Algorithm.Read(FileNode)

Algorithm.Save(string)

CvPtrObject.RawPtr

CvObject.IsDisposed

CvObject.ptr

CvObject.SetSafeHandle(OpenCvSafeHandle)

CvObject.SetPostReleaseAction(Action)

CvObject.Dispose()

CvObject.Dispose(bool)

CvObject.DisposeManaged()

CvObject.DisposeUnmanaged()

CvObject.ThrowIfDisposed()

CvObject.CvPtr

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Properties

Extended: false means that the basic descriptors (64 elements each) shall be computed. true means that the extended descriptors (128 elements each) shall be computed

HessianThreshold: Threshold for the keypoint detector. Only features, whose hessian is larger than hessianThreshold are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast.

NOctaveLayers: The number of images within each octave of a gaussian pyramid. It is set to 2 by default.

NOctaves: The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it.

Upright: false means that detector computes orientation of each feature. true means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting upright=true.