Dear all,
the next scikit-image developers meeting will take place online
this Friday (07-31) at 9AM, Paris time (CEST). The link to join the
meeting is https://monash.zoom.us/j/284282585. A tentative agenda can be
found on https://hackmd.io/r5oMbIyvQH2K0mhgntAW2g?both.
All scikit-image contributors are welcome to participate to the
meeting.
All the best
Emma

I cant tell from the abstract TBH. I'd have to do some digging (when I get back from holidays). I'm sure I've cited it in my own scientific work at the time.
- Almar
On Thu, Jul 16, 2020, at 17:09, Brenda Prallon wrote:
> Hi Almar,
>
> Thank you so much for your quick response. Do you remember by any chance if the paper was the following one?
> https://doi.org/10.1002/net.3230070404
>
> Once again, thank you!
>
> Em qua., 15 de jul. de 2020 às 17:57, Almar Klein <almar.klein(a)gmail.com> escreveu:
>> __
>> Hi Brenda,
>>
>> This is all from the top of my head because I dont have access to a computer right now. I recall that at its base the algorithm was based on a paper, which in essence described the application of Dijkstras algirithm on a regular grid (i.e. an image).
>>
>> So at its core its not new. It is implemented in a way that allows it to be used in a flexible way.
>>
>> The speed and memory efficiency can probably be attributed to (the implementation of) the binary heap.
>>
>> Regards,
>>
>> Almar
>>
>> On Wed, Jul 15, 2020, at 15:57, Brenda Prallon wrote:
>>> Hi everyone,
>>>
>>> I was hoping to get some theoretical details on the algorithm of find_costs for the MCP and MCP_Geometric classes. I am not a computer scientist, so I've had some trouble drawing conclusions from the source code alone; I hope someone can help me :)
>>>
>>> First, let me give you some context (although the questions don't depend on it): I am trying to find the most efficient way possible to solve the following problem: I have a (4359, 4950) raster and 5000 georeferenced points. I need to find the shortest path between all these points (basically, the output would be the triangle part of a symmetric matrix). I have run some tests using MCP, MCP_Geometric and the package "gdistance" in R, which claims to implement Djikistra's algorithm in the costDistance function (it actually relies on package "igraph", which use C written functions in the background as well). MCP.find_costs() and MC_Geometric.find_costs() have performed consistently better, even for a small sample of points (e.g. 10), so I am skeptical that the difference is due to R being slower than Python (plus, I've run the examples on a jupyter notebook). Not only that, but while MCP and MCP_Geometric don't use more than 1GB of RAM memory, the examples in R use more than 10GB.
>>>
>>> So, firstly, what I would like to ask is:
>>>
>>> 1- What is the algorithm implemented in MCP? Is it simply Djikstra's, some other known algorithm, or something new?
>>> 2- If it is something new, what would be the complexity?
>>> 3- How is it so memory efficient?
>>>
>>> At last I wanted to check if I got something straight: the difference of MCP.find_costs() to MCP_Geometric.find_costs() is just that in MCP_Geometric the matrix of costs needs to be recalculated before running the algorithm, is that right? Or does it affect the internal process of finding the shortest path in some other way?
>>>
>>> Thank you for reading this far :) This implementation seems very unique and efficient, and I am just trying to better understand it.
>>> --
>>> Brenda
>>> _______________________________________________
>>> scikit-image mailing list -- scikit-image(a)python.org
>>> To unsubscribe send an email to scikit-image-leave(a)python.org
>>> https://mail.python.org/mailman3/lists/scikit-image.python.org/
>>> Member address: almar.klein(a)gmail.com
>>>
>
>
> --
> Brenda

Hi everyone,
I was hoping to get some theoretical details on the algorithm of find_costs
for the MCP and MCP_Geometric classes. I am not a computer scientist, so
I've had some trouble drawing conclusions from the source code alone; I
hope someone can help me :)
First, let me give you some context (although the questions don't depend on
it): I am trying to find the most efficient way possible to solve the
following problem: I have a (4359, 4950) raster and 5000 georeferenced
points. I need to find the shortest path between all these points
(basically, the output would be the triangle part of a symmetric matrix). I
have run some tests using MCP, MCP_Geometric and the package "gdistance" in
R, which claims to implement Djikistra's algorithm in the costDistance
function (it actually relies on package "igraph", which use C written
functions in the background as well). MCP.find_costs() and
MC_Geometric.find_costs() have performed consistently better, even for a
small sample of points (e.g. 10), so I am skeptical that the difference is
due to R being slower than Python (plus, I've run the examples on a jupyter
notebook). Not only that, but while MCP and MCP_Geometric don't use more
than 1GB of RAM memory, the examples in R use more than 10GB.
So, firstly, what I would like to ask is:
1- What is the algorithm implemented in MCP? Is it simply Djikstra's, some
other known algorithm, or something new?
2- If it is something new, what would be the complexity?
3- How is it so memory efficient?
At last I wanted to check if I got something straight: the difference of
MCP.find_costs() to MCP_Geometric.find_costs() is just that in
MCP_Geometric the matrix of costs needs to be recalculated before running
the algorithm, is that right? Or does it affect the internal process of
finding the shortest path in some other way?
Thank you for reading this far :) This implementation seems very unique and
efficient, and I am just trying to better understand it.
--
Brenda

Dear all,
there will be two days of sprints at the end of the Scipy
conference, on the weekend of July 11-12. We would like to set up a
sprint for scikit-image, but for this we would like to know if people can
be available to help mentoring new contributors, including
pair-programming. Having a couple of scikit-image maintainers available
would be ideal, but the help of other experienced devs is very much
welcome (basically if you already had a PR merged in scikit-image or in
a comparable project probably you have the knowledge required to help
someone else). We expect that sprinters will be active mostly during
American business hours but probably there will be also people from other
time zones.
Please answer here if you wish to participate to the sprint, in
which time zone, and in particular if you can help new contributors!
Best
Emma