SIFT point corner feature object
A subclass of OrientedScalePointFeature for SIFT features.
| plot | Plot feature position |
| plot_scale | Plot feature scale |
| distance | Descriptor distance |
| ncc | Descriptor similarity |
| match | Match features |
| ncc | Descriptor similarity |
| uv | Return feature coordinate |
| display | Display value |
| char | Convert value to string |
| u | horizontal coordinate |
| v | vertical coordinate |
| strength | feature strength |
| theta | feature orientation [rad] |
| scale | feature scale |
| descriptor | feature descriptor (vector) |
| image_id | index of image containing feature |
Properties of a vector of SiftCornerFeature objects are returned as a vector. If F is a vector (Nx1) of SiftCornerFeature objects then F.u is a 2xN matrix with each column the corresponding u coordinate.
"Distinctive image features from scale-invariant keypoints", D.Lowe, Int. Journal on Computer Vision, vol.60, pp.91-110, Nov. 2004.
isift, PointFeature, ScalePointFeature, orientedscalepointfeature, SurfPointFeature
Create a SIFT point feature object
f = SiftPointFeature() is a point feature object with null parameters.
f = SiftPointFeature(u, v) is a point feature object with specified coordinates.
f = SiftPointFeature(u, v, strength) as above but with specified strength.
f = SiftPointFeature(u, v, strength, scale) as above but with specified feature scale.
f = SiftPointFeature(u, v, strength, scale, theta) as above but with specified feature orientation.
Match SIFT point features
m = F.match(f2, options) is a vector of FeatureMatch objects that describe candidate matches between the two vectors of SIFT features F and f2. Correspondence is based on descriptor similarity.
Support region of feature
out = F.support(im, w) is an image of the support region of the feature F, extracted from the image im in which the feature appears. The support region is scaled to WxW and rotated so that the feature's orientation axis is upward.
out = F.support(images, w) as above but if the features were extracted from an image sequence images then the feature is extracted from the appropriate image in the same sequence.
[out,T] = F.support(images, w) as above but returns the pose of the feature as a 3x3 homogeneous transform in SE(2) that comprises the feature position and orientation.
F.support(im, w) as above but the support region is displayed.
Machine Vision Toolbox for MATLAB © 1990-2014 Peter Corke.