View source: R/roofEdgeParSel.r

roofEdgeParSel | R Documentation |

Select bandwidth and threshold value for the roof/valley edge detector using bootstrap.

```
roofEdgeParSel(image, bandwidth, thresh, nboot, edge1, blur = FALSE)
```

`image` |
A square matrix object of size n by n, no missing value allowed. |

`bandwidth` |
Positive integers to specify the number of pixels used in the local smoothing. These are the bandwidth parameters to be chosen from. |

`thresh` |
Threshold values to be chosen from. |

`nboot` |
Number of bootstrap samples. |

`edge1` |
Step edges. The function excludes step edges when detect roof/valley edges. |

`blur` |
TRUE if the image contains blur, FALSE otherwise. |

If *blur=TRUE*, then a conventional local linear kernel smoothing is
applied to estimate the blurred surface; Bootstrap samples are obtained by
drawing with replacement from the residuals and the `d_{KQ}`

is computed
for the detected edges of the original sample and those of the bootstrap
samples. If *blur=FALSE*, the procedure is the same as when *blur=TRUE*
except that a jump-preserving kernel smoothing procedure is used to obtain
residuals.

Returns a list of the selected bandwdith, the selected threshold value,
and a matrix of `d_{KQ}`

values with each entry corresponding to each combination
of bandwdith and threshold.

Qiu, P. and Kang, Y. (2015) “Blind Image Deblurring Using Jump Regression
Analysis”, *Statistica Sinica*, **25**, 879-899,
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.5705/ss.2014.054")}

`roofDiff`

, `roofEdge`

```
## Not run:
step.edges <- stepEdge(peppers, bandwidth = 9, thresh = 17)
set.seed(24)
parSel <- roofEdgeParSel(image = peppers, bandwidth = 5, thresh = 5000,
nboot = 1, edge1 = step.edges, blur = TRUE) # Time Consuming
## End(Not run)
```

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