Visual reconstruction
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Kernel density estimation with adaptive varying window size
Pattern Recognition Letters
Features in Scale Space: Progress on the 2D 2nd Order Jet
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Properties of Brownian image models in scale-space
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Hypotheses for Image Features, Icons and Textons
International Journal of Computer Vision
Critical Scale for Unsupervised Cluster Discovery
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Scale-space clustering with recursive validation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Image features and the 1-D, 2nd
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps
Computers in Biology and Medicine
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Estimation of the mode of a distribution over Rn from discrete samples is introduced and three methods for its solution are developed and evaluated. The first solution is based on Fréchet's definition of central tendencies. We show that algorithms based on this approach have only limited success due to the non-differentiability of the Fréchet measures. The second solution is based on tracking maxima through a Scale Space built from the samples. We show that this is more accurate than the Fréhet approach, but that tracking to very fine scales is unwarranted and undesirable. For our third method we analyze the reliability of the information across scale using an exact bootstrap analysis. This leads to a modified version of the Scale Space approach where unreliable information is downgraded (pessimistically) so that tracking into such regions does not occur. This modification improves the accuracy of mode estimation. We conclude with demonstrations on high-dimensional real and synthetic data, which confirm the technique's accuracy and utility.