Indexing based on scale invariant interest points
Mikolajczyk, K.; Schmid, C.
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Volume 1, Issue , 2001 Page(s):525 - 531 vol.1
Digital Object Identifier 10.1109/ICCV.2001.937561
Summary:This paper presents a new method for detecting scale invariant
interest points. The method is based on two recent results on scale
space: (1) Interest points can be adapted to scale and give repeatable
results (geometrically stable). (2) Local extrema over scale of
normalized derivatives indicate the presence of characteristic local
structures. Our method first computes a multi-scale representation for
the Harris interest point detector. We then select points at which a
local measure (the Laplacian) is maximal over scales. This allows a
selection of distinctive points for which the characteristic scale is
known. These points are invariant to scale, rotation and translation as
well as robust to illumination changes and limited changes of viewpoint.
For indexing, the image is characterized by a set of scale invariant
points; the scale associated with each point allows the computation of a
scale invariant descriptor. Our descriptors are, in addition, invariant
to image rotation, of affine illumination changes and robust to small
perspective deformations. Experimental results for indexing show an
excellent performance up to a scale factor of 4 for a database with more
than 5000 images
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