  We present the CPMA, a new method for medial axis pruning with noise
robustness and equivariance to isometric transformations. Our method leverages
the discrete cosine transform to create smooth versions of a shape $\Omega$. We
use the smooth shapes to compute a score function $\scorefunction$ that filters
out spurious branches from the medial axis. We extensively compare the CPMA
with state-of-the-art pruning methods and highlight our method's noise
robustness and isometric equivariance. We found that our pruning approach
achieves competitive results and yields stable medial axes even in scenarios
with significant contour perturbations.
