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Enhanced Guo-Hall Thinning Algorithm Thin Binary Image using Guo-Hall Algorithm (Internal)

Source: R/Thinning4.R
thin_image_guohall.Rd

This internal function applies the Guo-Hall thinning algorithm to reduce binary images to their skeletons while preserving connectivity and structure.

Usage

thin_image_guohall(img, verbose = FALSE, select.layer = 2)

Arguments

img

A matrix, data frame, or `SpatRaster` object representing the binary image to be thinned.

verbose

Logical. If `TRUE`, outputs diagnostic information such as image dimensions, pixel removal counts, and iteration progress. Default is `FALSE`.

select.layer

Integer indicating the layer to use if `img` is a multi-layer `SpatRaster`. Default is 2.

Value

A binary matrix representing the thinned image (skeleton).

Details

- The input image is first processed using load_flexible_image to ensure it is a binary matrix. - Thinning is performed in an iterative process consisting of two subiterations per cycle: 1. In the first subiteration, pixels are marked for removal based on specific Guo-Hall conditions. 2. In the second subiteration, a different set of conditions is applied to mark additional pixels for removal. - The process continues until no pixels are removed in an iteration or the maximum number of iterations (default: 1000) is reached. - The Guo-Hall algorithm ensures that the skeleton of the image is preserved.

Examples

if (FALSE) { # \dontrun{
# Example usage
raster <- terra::rast(matrix(c(0, 1, 1, 0, 0, 1, 1, 0,0), nrow = 3))
thinned_image <- thin_image_guohall(raster, verbose = TRUE)
} # }