profoundSegimMerge: Merge Segmentation Maps

Description

Takes two segmentation maps and merges them in a sensible manner, making sure segments representing the same object are not overlaid on each other.

Usage

profoundSegimMerge(image = NULL, segim_base = NULL, segim_add = NULL, mask = NULL,
sky = 0)

Arguments

image

Numeric matrix; required, the image we want to analyse. Note, image NAs are treated as masked pixels.

segim_base

Integer matrix; required, the base segmentation map of the image. This matrix *must* be the same dimensions as image.

segim_add

Integer matrix; required, the new segmentation map of the image that is to be added. This matrix *must* be the same dimensions as image.

mask

Boolean matrix; optional, parts of the image to mask out (i.e. ignore), where 1 means mask out and 0 means use for analysis. If provided, this matrix *must* be the same dimensions as image.

sky

User provided estimate of the absolute sky level. Can be a scalar or a matrix matching the dimensions of image (allows values to vary per pixel). This will be subtracted off the image internally, so only provide this if the sky does need to be subtracted!

Value

Integer matrix; the merged segmentation map matched pixel by pixel to image.

Details

The merger strategy is quite simple. Matching object segments are identified by the 'uniqueID' ID from an internal run of profoundSegimStats. Whichever segment contains more flux is determined to be the best map to use as the base segment. Unmatched segments in the segim_add map are added back in after this initial merging process, so will end up on top and potentially appear as segment islands within larger segments (which is not possible using the standard segmentation process in profoundMakeSegim).

An obvious reason to use this function is in situations where bright stars are embedded deep within an extended source. The standard watershed segmentation used in profoundMakeSegim will tend to break a large portion of the extended source off to form the segmented region. By running profoundProFound in different modes it is possible to identify the bright peaks (see Examples below), and then use profoundSegimMerge to piece the segments back together appropriately.

Examples

Run this code

# NOT RUN {
image=readFITS(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits',
package="ProFound"))$imDat
profound=profoundProFound(image, plot=TRUE)
profound_diff=profoundProFound(profoundImDiff(image, sigma=2), plot=TRUE)
tempmerge=profoundSegimMerge(image, profound$segim, profound_diff$segim)

#Notice the new embedded blue segment near the centre:

profoundSegimPlot(image, segim=tempmerge)
# }