This repository contains the official implementations of the CPMA/C-CPMA medial axis pruning. The CPMA, is a method for medial axis pruning with noise robustness and equivariance to isometric transformations. It leverages the Discrete Cosine Transform to create smooth versions of a shape S. We use the smooth shapes to compute a score function F(x, S) that filters out spurious branches from the medial axis of the original shape. Our method generalizes to n-dimensional shapes given the properties of the Discrete Cosine Transform.
After cloning the repository, you will need to install all the necessary packages. We recommend creating a new conda environment using the environment.yml file we provide:
conda env create -f environment.yml
conda activate cpma
After you install all the dependencies and create the conda environment, you can run the 2D test file as:
python run_2d_test.py
The command should create a new folder named results. Inside this folder you will see a comparative figure for every image in the data folder. The comparative images should look like this:
COMING SOON!
If you find our code or our paper useful, please consider citing it.
@article{Patino2021CPMA,
title={Cosine-Pruned Medial Axis: A New Method for Isometric Equivariant and Noise-Free Medial Axis Extraction},
author={Diego Alberto Patiño Cortes and John Willian Branch},
journal={IEEE Access},
year={2021},
volume={9},
pages={65466-65481}
}