Documentation>MATLAB API>SIFT - vl_sift

F = VL_SIFT(I) computes the SIFT frames [1] (keypoints) F of the image I. I is a gray-scale image in single precision. Each column of F is a feature frame and has the format [X;Y;S;TH], where X,Y is the (fractional) center of the frame, S is the scale and TH is the orientation (in radians).

[F,D] = VL_SIFT(I) computes the SIFT descriptors [1] as well. Each column of D is the descriptor of the corresponding frame in F. A descriptor is a 128-dimensional vector of class UINT8.

VL_SIFT() accepts the following options:

Octaves maximum possible

  Set the number of octave of the DoG scale space.

Levels 3

  Set the number of levels per octave of the DoG scale space.

FirstOctave 0

  Set the index of the first octave of the DoG scale space.

PeakThresh 0

  Set the peak selection threshold.

EdgeThresh 10

  Set the non-edge selection threshold.

NormThresh -inf

  Set the minimum l2-norm of the descriptors before normalization. Descriptors below the threshold are set to zero.

Magnif 3

  Set the descriptor magnification factor. The scale of the keypoint is multiplied by this factor to obtain the width (in pixels) of the spatial bins. For instance, if there are there are 4 spatial bins along each spatial direction, the ``side'' of the descriptor is approximatively 4 * MAGNIF.

WindowSize 2

  Set the variance of the Gaussian window that determines the descriptor support. It is expressend in units of spatial bins.

Frames

  If specified, set the frames to use (bypass the detector). If frames are not passed in order of increasing scale, they are re-orderded.

Orientations

  If specified, compute the orientations of the frames overriding the orientation specified by the 'Frames' option.

Verbose

  If specfified, be verbose (may be repeated to increase the verbosity level).

REFERENCES

  [1] D. G. Lowe, Distinctive image features from scale-invariant keypoints. IJCV, vol. 2, no. 60, pp. 91-110, 2004.

See also: SIFT VL_UBCMATCH(), VL_DSIFT(), VL_HELP().