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1 | % SIFT_OVERVIEW Scale-Invariant Feature Transfrom | ||
2 | % | ||
3 | % This is a MATLAB/C implementation of SIFT detector and descriptor | ||
4 | % [1]. You can: | ||
5 | % | ||
6 | % * Use SIFT() to detect the SIFT frames (keypoints) of a given image | ||
7 | % and compute their descriptors. Then you can use SIFTMATCH() to | ||
8 | % match the descriptors. | ||
9 | % | ||
10 | % * Use PLOTSS(), PLOTSIFTDESCRIPTOR(), PLOTSIFTFRAME(), | ||
11 | % PLOTMATCHES() to visualize the results. | ||
12 | % | ||
13 | % As SIFT is implemented by several reusable M and MEX files, you can | ||
14 | % also run portions of the algorithm, or change them. Specifically, | ||
15 | % you can: | ||
16 | % | ||
17 | % * Use SIFTDESCRIPTOR() to compute the SIFT descriptor from a list | ||
18 | % of frames and a scale space or plain image. | ||
19 | % | ||
20 | % * Use GAUSSIANSS() and DIFFSS() to compute the Gaussian and DOG | ||
21 | % scale spaces. | ||
22 | % | ||
23 | % * Use SIFTLOCALMAX(), SIFTREFINEMX(), SIFTORMX() to manually | ||
24 | % extract the SIFT frames from the DOG scale space. More in | ||
25 | % general, you can use SIFTLOCALMAX() to find maximizers of any | ||
26 | % multi-dimensional arrays. | ||
27 | % | ||
28 | % REFERENCES | ||
29 | % [1] D. G. Lowe, "Distinctive image features from scale-invariant | ||
30 | % keypoints," IJCV, vol. 2, no. 60, pp. 91 110, 2004. | ||
31 | % | ||
32 | % See also PDF:SIFT.INTRODUCTION. | ||
33 | |||