| Authors |
Donoser Michael, Kluckner Stefan , Bischof Horst |
| Appeared in |
Proceedings International Conference on Pattern Recognition |
| Date |
2010 |
| Abstract |
Covariance matrices have recently been a popular
choice for versatile tasks like recognition and tracking
due to their powerful properties as local descriptor and
their low computational demands. This paper outlines
similarities of covariance matrices to the well-known
structure tensor. We show that the generalized version
of the structure tensor is a powerful descriptor and that
it can be calculated in constant time by exploiting the
properties of integral images. To measure the similarities
between several structure tensors, we describe an
approximation scheme which allows comparison in a
Euclidean space. Such an approach is also much more
efficient than the common, computationally demanding
Riemannian Manifold distances. Experimental evaluation
proves the applicability for the task of object tracking
demonstrating improved performance compared to
covariance tracking. |
| Link |
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