# stepDiff: Step Edge Detection Statistics In DRIP: Discontinuous Regression and Image Processing

 stepDiff R Documentation

## Step Edge Detection Statistics

### Description

Compute difference between two one-sided local kernel estimates along the gradient direction.

### Usage

``````stepDiff(image, bandwidth, degree = 1, blur = FALSE, plot = FALSE)
``````

### Arguments

 `image` A square matrix, no missing value allowed. `bandwidth` A positive integer that specifies the number of pixels to use in the local smoothing. `degree` An integer equal to 0 for local constant kernel smoothing or 1 for local linear kernel smoothing. The default value is 1. `blur` If blur = TRUE, in addition to a conventional 2-D kernel function, a 1-D kernel is used in local smoothing to address the issue of blur. The default value is FALSE. `plot` If plot = TRUE, an image of the detection statistics at each pixel is plotted.

### Details

At each pixel, the gradient is estimated by a local linear kernel smoothing procedure. Next, the local neighborhood is divided into two halves along the direction perpendicular to (`\widehat{f}'_{x}`, `\widehat{f}'_{y}`). Then the one- sided local kernel estimates are obtained in the two half neighborhoods respectively.

### Value

A matrix of the estimated difference, `|\widehat{f}_+ - \widehat{f}_-|`, at each pixel.

### References

Kang, Y. and Qiu, P. (2014) "Jump Detection in Blurred Regression Surfaces," Technometrics, 56(4), 539 – 550, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2013.844732")}.

`roofDiff`
``````data(sar) # SAR image is bundled with the package and it is a