HPWmap

Pointwise (PW) probabilities and highest pointwise (HPW) probabilities of
all differences of smooths at neighboring scales are computed.

A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
To infer which of the captured features are credible, Bayesian analysis is used.
The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
(2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
(3) Posterior credibility analysis of the differences of smooths created.
The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

Roman Flury

mrbsizeR

Scale Space Multiresolution Analysis of Random Signals

Thimo Schuster

Leena Pasanen

Reinhard Furrer

HPWmap function

<dl><dt>smoothVec</dt>
<dd>Differences of smooths at neighboring scales.</dd>
<dt>mm</dt>
<dd>Number of rows of the original input image.</dd>
<dt>nn</dt>
<dd>Number of columns of the original input image.</dd>
<dt>prob</dt>
<dd>Credibility level for the posterior credibility analysis</dd></dl>

Arguments

Computation of pointwise and highest pointwise probabilities. — HPWmap

<dl>

<dt>smoothVec</dt>
<dd>Differences of smooths at neighboring scales.</dd>


<dt>mm</dt>
<dd>Number of rows of the original input image.</dd>


<dt>nn</dt>
<dd>Number of columns of the original input image.</dd>


<dt>prob</dt>
<dd>Credibility level for the posterior credibility analysis</dd>

</dl>

HPWmap: Computation of pointwise and highest pointwise probabilities.

Description

Usage

Value

Arguments

Details

Examples