Statistical scale space methods

Holmström, Lasse; Ruha (née Pasanen), Leena (2016-07-22)
 
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URL:
https://doi.org/10.1111/insr.12155

Holmström, Lasse
Ruha (née Pasanen), Leena
John Wiley & Sons
22.07.2016

Holmström, L., and Pasanen, L. (2017) Statistical Scale Space Methods. International Statistical Review, 85: 1–30. doi: 10.1111/insr.12155

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© 2016 The Authors. International Statistical Review © 2016 International Statistical Institute. Published in this repository with the kind permission of the publisher.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1111/insr.12155
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https://urn.fi/URN:NBN:fi-fe201704066003
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Summary

The goal of statistical scale space analysis is to extract scale-dependent features from noisy data. The data could be for example an observed time series or digital image in which case features in either different temporal or spatial scales would be sought. Since the 1990s, a number of statistical approaches to scale space analysis have been developed, most of them using smoothing to capture scales in the data, but other interpretations of scale have also been proposed. We review the various statistical scale space methods proposed and mention some of their applications.

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