Rvision (version 0.6.0)

watershed: Image Segmentation Using the Watershed Algorithm

Description

watershed implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in Meyer (1992).

Usage

watershed(image, markers)

Arguments

image

An 8-bit (8U), BGR Image object.

markers

A signed 32-bit (32S) single-channel (GRAY) Image object (see Details).

Value

This function does not return anything. It modifies markers in place.

Details

Before passing image to the function, you have to roughly outline the desired regions in the markers image with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using findContours. The markers are "seeds" of the future image regions. All the other pixels in markers, whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.

References

Meyer F. Color image segmentation. 1992 International Conference on Image Processing. 1992. Available: https://ieeexplore.ieee.org/abstract/document/785528/

See Also

findContours

Examples

# NOT RUN {
dots <- image(system.file("sample_img/dots.jpg", package = "Rvision"))
bw <- inRange(dots, 0, 250)
medianBlur(bw, target = "self")
sure_bg <- morph(bw, "dilate", k_shape = "ellipse", iterations = 3)
dt <- distanceTransform(bw, "L2")
sure_fg <- dt > 20
unknown <- sure_bg - sure_fg
markers <- connectedComponents(sure_fg, table = FALSE)$labels + 1
markers <- markers * (invert(unknown) / 255)
watershed(dots, markers)

# }