Santiago VELASCO-FORERO Personal Website
In this study, we propose a new methodology that leverages the Choquet capacity, a mathematical tool from the theory of capacities, to capture the intricate properties of point patterns. By incorporating a dilation model, we enhance the ability to represent the spatial arrangement and density variations in the Neyman-Scott point process accurately. To validate the effectiveness of our approach, extensive experiments were conducted on synthetic datasets simulating diverse spatial point patterns on cell. The dilation model applied on real data with the distance function. We achieved the accuracy of
$90%$ which out performed CNN. The method is far faster than the other deep learning methods.
You can download a copy of all the files in the repository morpholayersby cloning the repository:
git clone https://github.com/Jacobiano/ChoquetLayer.git
Tensorflow 2.0>=
train_capacity2 can be use to generate the paper results.
Note: In the first run, you have to keep --generatedate argument to 1 to generate simulations.
The authors gratefully acknowledge the financial support of Institut Carnot (grant 220000496).
This work was granted access to the HPC resources of IDRIS under the allocation 2023-AD011012212R2 made by GENCI
MIT License
Copyright (c) 2020 Santiago Velasco-Forero
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