modify1: Edge detection, post processing

In DRIP: Discontinuous Regression and Image Processing

Edge detection, post processing

Description

Modify detected edges to make them thin.

Usage

modify1(bandwidth, image, edge, plot)

Arguments

image	A matrix that represents the image.
bandwidth	A positive integer to specify the number of pixels used in the local smoothing.
edge	A matrix of 0 and 1 represents detected edge pixels.
plot	If plot = TRUE, images of detected edges before the modification and after the modification are plotted.

Details

A local-smoothing based edge detection algorithm may flag deceptive edge pixel candidates. One kind of such candidates consists of those close to the real edges. They occur due to the nature of local smoothing. That is, if the point (x_i, y_j) is flagged, then its neighboring pixels will be flagged with high probability. This kind of deceptive candidates can make the detected edges thick. This modification procedure makes the detected edges thin.

Value

Returns a matrix of zeros and ones of the same size as edge.

References

Qiu, P. and Yandell, B., "Jump detection in regression surfaces," Journal of Computational and Graphical Statistics 6(3), 1997, 332-354.

See Also

modify2

Examples

data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
edge = stepEdgeLCK(sar, 4, 20)
out = modify1(4, sar, edge)

DRIP documentation built on April 7, 2022, 1:06 a.m.