Indexing Based on Scale Invariant Interest Points

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Indexing Based on Scale Invariant Interest Points
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, to affine ...
Krystian Mikolajczyk, Cordelia Schmid
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2001
Where ICCV
Authors Krystian Mikolajczyk, Cordelia Schmid
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