GNU Astronomy Utilities - Tasks: task #14980, Better way to grow true clumps...
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task #14980: Better way to grow true clumps (full segmentation/deblending)
Submitter: | Mohammad Akhlaghi <makhlaghi> | ||
Submitted: | Fri 06 Jul 2018 11:32:15 AM UTC | ||
Should Start On: | Thu 05 Jul 2018 10:00:00 PM UTC | Should be Finished on: | Thu 05 Jul 2018 10:00:00 PM UTC |
Category: | Segment | Priority: | 5 - Normal |
Item Group: | Enhancement | Status: | Postponed |
Privacy: | Public | Assigned to: | None |
Percent Complete: | 0% | Open/Closed: | Open |
Effort: | 0.00 |
Tue 30 Oct 2018 12:58:08 PM UTC, comment #3: |
Mohammad Akhlaghi <makhlaghi> |
Sun 29 Jul 2018 09:46:12 PM UTC, comment #2: The attached slides PDF file was apparently not submitted in the previous comment. The slides are also available in this link:
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Mohammad Akhlaghi <makhlaghi> |
Sun 29 Jul 2018 09:41:10 PM UTC, comment #1: As a complement to the main task description, this task is about how the true peaks are "grown", not about detection (separating astronomical objects from the background) or finding true peaks.
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Mohammad Akhlaghi <makhlaghi> |
Fri 06 Jul 2018 11:32:15 AM UTC, original submission:
In Segment, after true clumps are found, they are "grown" simply by sorted pixels (as you see in Figure 10 of the NoiseChisel paper).
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Mohammad Akhlaghi <makhlaghi> |
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Follow 5 latest changes.
Date | Changed by | Updated Field | Previous Value | => | Replaced by |
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2018-09-19 | makhlaghi | Summary | Max-trees to grow true clumps | Better way to grow true clumps (full segmentation/deblending) | |
2018-07-19 | makhlaghi | Carbon-Copy | Removed -email is unavailable- | - | |
2018-07-19 | makhlaghi | Carbon-Copy | Removed -email is unavailable- | - | |
2018-07-06 | makhlaghi | Carbon-Copy | - | Added -email is unavailable- | |
Carbon-Copy | - | Added -email is unavailable- |
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Corresponding source code
This is also an interesting non-parametric (but machine-learning based: hard to configure) deblending method which we might be able to implement (after building a machine learning infra-structure within Gnuastro):
https://arxiv.org/abs/1810.10098
Since we know the number of clumps in each detection, we know the ultimate number of objects to deblend. In a large survey, we can also use this information to find detections with one or no clumps (which we may assume are not blended) and use them to define our training set.