Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
The discrepancy method: randomness and complexity
The discrepancy method: randomness and complexity
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Quasi-random integration in high dimensions
Mathematics and Computers in Simulation
Monomodal image registration using mutual information based methods
Image and Vision Computing
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A Similarity Measure for Image and Volumetric Data Based on Hermann Weyl's Discrepancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Divergences and Informations in Statistics and Information Theory
IEEE Transactions on Information Theory
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The effect of non-monotonicity of similarity measures is addressed which can be observed when measuring the similarity between patterns with increasing displacement. This effect becomes the more apparent the less smooth the pattern is. It is proven that commonly used similarity measures like f-divergence measures or kernel functions show this non-monotonicity effect which results from neglecting any ordering in the underlying construction principles. As an alternative approach Weyl's discrepancy measure is examined by which this nonmonotonicity effect can be avoided even for patterns with high-frequency or chaotic characteristics. The impact of the non-monotonicity effect to applications is discussed by means of examples from the field of stereo matching, texture analysis and tracking.