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Robust Defect Identification
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Sbalzarini Lab
Software
Bio-Imaging
Robust Defect Identification
Commits
70eef229
Commit
70eef229
authored
1 year ago
by
Karl Hoffmann
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Add irregular case in README
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@@ -10,6 +10,10 @@ The code was developed by Karl B. Hoffmann during his PhD studies under supervis
For usage example go through the main Jupyter Notebook
[
robust_defect_identification.ipynb
](
./robust_defect_identification.ipynb
)
or have a look at the
[
examples section
](
./README.md#example-workflows
)
.
The methods do
**not**
depend on a square grid as is shown
in the Jupyter Notebook
[
robust_defect_identification_irregular.ipynb
](
./robust_defect_identification_irregular.ipynb
)
.
with an irregular set of discretization points.
In fact, any gridded data can also be treated in this more general way.
## Citation
When using the software, please cite
...
...
@@ -85,6 +89,11 @@ PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.*
Output plots of the main Jupyter Notebook
[
robust_defect_identification.ipynb
](
./robust_defect_identification.ipynb
)
for
the different examples follow below. They can be reproduced in
[
robust_defect_identification.ipynb
](
./robust_defect_identification.ipynb
)
starting from the initial input image.
The synthethic example data is also available with an irregular set of discretization points
in the Jupyter Notebook
[
robust_defect_identification_irregular.ipynb
](
./robust_defect_identification_irregular.ipynb
)
.
There, parts of the effort are in building up the correct structures (in fact, pandas DataFrames) to
represent the planar graph formed by the discretization points.
Each example
-
starts from original grayscale image (except for the synthethic data)
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