Original Article
Statistical Scale Space Methods1
Lasse Holmström,
Leena Pasanen,
Lasse Holmström
Department of Mathematical Sciences, University of Oulu, Finland
Search for more papers by this authorLeena Pasanen
Department of Mathematical Sciences, University of Oulu, Finland
Search for more papers by this authorLasse Holmström,
Leena Pasanen,
Lasse Holmström
Department of Mathematical Sciences, University of Oulu, Finland
Search for more papers by this authorLeena Pasanen
Department of Mathematical Sciences, University of Oulu, Finland
Search for more papers by this author1
This paper is followed by discussions and a rejoinder.
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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