Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
On a non-monotonicity effect of similarity measures
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Hierarchical kernel-based rotation and scale invariant similarity
Pattern Recognition
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The paper focuses on similarity measures for translationally misaligned image and volumetric patterns. For measures based on standard concepts such as cross-correlation, L_p-norm, and mutual information, monotonicity with respect to the extent of misalignment cannot be guaranteed. In this paper, we introduce a novel distance measure based on Hermann Weyl's discrepancy concept that relies on the evaluation of partial sums. In contrast to standard concepts, in this case, monotonicity, positive-definiteness, and a homogenously linear upper bound with respect to the extent of misalignment can be proven. We show that this monotonicity property is not influenced by the image's frequencies or other characteristics, which makes this new similarity measure useful for similarity-based registration, tracking, and segmentation.