| Author(s) |
Bernhard Moser
Tomáš Kazmar
Peter Haslinger
|
| Title |
On the potential of Hermann Weyl's discrepancy norm for texture analysis |
| Booktitle |
Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008) |
| Typ |
Inproceedings |
| Month |
December |
| Year |
2008 |
| Pages |
187-191 |
| Editor(s) |
M. Masoud Mohammadian |
| Publisher |
IEEE Computer Society |
| ISBN |
1-4244-3329-0 |
| SCCH # |
0835 |
| The paper focuses on similarity-based texture classification
and analysis techniques. A novel similarity measure is
introduced in this context that takes also structural spatial
information of the intensity distribution of the textured image
into account which turns out to be advantageous compared
to standard concepts as for example pixel-by-pixel
based similarity measures like cross-correlation or statistics
based measures like the Bhattacharyya coefficient. The
introduced measure relies on the evaluation of partial sums
and can be computed in linear time based on integral images.
It is a crucial property of this measure that for integrable
(non-periodic) functions it can be proven that the
auto-correlation based on this measure shows monotonicity
with respect to the amount of spatial shift. In this paper
experimental studies with regular textures demonstrate
the usefulness of applying this measure to the problem of
texture classification and analysis, further its well-behavior
regarding noise is outlined. |