# stepEdge: Step Edge Detector In DRIP: Discontinuous Regression and Image Processing

 stepEdge R Documentation

## Step Edge Detector

### Description

Detect step edges in an image.

### Usage

``````stepEdge(image, bandwidth, thresh, 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. `thresh` The threshold value to use in the edge detection criterion. Must be a positive value. `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 detected edges is plotted.

### Details

At each pixel, the gradient is estimated by a local 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. The pixel is flagged as a step edge pixel if `|\widehat{f}_+ - \widehat{f}_-|>u`, where `u` is the specified threshold value.

### Value

A matrix of zeros and ones. Ones represent the detected edge pixels and zeros represent the non-edge pixels.

### 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`, `stepDiff`, `roofEdge`
``````data(sar)