R devel - Oct 2020 - Could "MedicalImaging" Be Changed to "ImageProcessing"?

Dear List,

Regarding the task views:
I was wondering if it would be possible to replace the MedicalImaging
task view with an ImageProcessing task view?
Alternatively, a separate task view could be created.
However, I suspect that there would be considerable overlap.
In my opinion, a single task view for image processing, would be preferable.

Also, I note that the Spatial task view contains a section on raster data.
This seems like the wrong place.
i.e. A raster file, typically represents a photograph or other image-like data.
Hence, it seems more natural for it to be in an image processing task
view, than in a spatial task view.
But then there is a counter argument to that, if it's not covered in
any other task view, then it needs to go somewhere. And the spatial
task view is preferrable, to nowhere.

When I think of spatial data, I think of things like coordinates for
weather satellites, or possibly a wireframe mesh. I suppose if a
raster file represented a matrix of height or depth values, then the
notion of image and spatial data coincide. But then there's a counter
argument to that, that the data should just be a plain text file with
a matrix, not a raster file.

Also, I note that the section on raster data needs to be updated.

A quick search resulted in a number of hits recommending two packages,
sp and raster.
However, a more thorough search resulted in more options, such as the
png and jpeg packages.
(Which have been on CRAN for about a decade).

I haven't tried the png and jpeg packages yet, but they seem like
attractive options.
So, I should be given them a test drive later today, or maybe tomorrow.

One final comment.
As a somewhat broad generalization, there seems to be considerably
more overlap between machine learning and image processing
(specifically), than there is between machine learning and spatial
data (more generally). Hence image processing should be given higher
precedence than what it currently is, and information on image
processing should be presented in a way that's suitable to parties
interested in machine learning.