Feature detection using Scale-invariant feature transform (SIFT)

an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.

images and details from (based on my understanding):
Lowe, David G. “Distinctive Image Features from Scale­ Invariant Keypoints”. International Journal of Computer Vision, 60, 2 (2004)

Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving.

Source: http://en.wikipedia.org/wiki/Scale-invariant_feature_transform


Results
TO DO: publish source code (Pure matlab)
Leave a comment for the source code


% Computation of the maximum number of levels:
Lmax = floor(min(log(2*size(img)/12)/log(scl)));

%build image pyramid and difference of gaussians filter response pyramid
[pyr,imp] = build_pyramid(img,Lmax,scl);
%get the feature points
pts = find_features(pyr,img,scl,thresh,radius,radius2,disp_flag,1);

%classify points and create sub-pixel and sub-scale adjustments
[features,keys] = refine_features(img,pyr,scl,imp,pts,radius3,min_sep,edgeratio);