Talks and Poster Presentations (with Proceedings-Entry):
C. Chen, H. Edelsbrunner:
"Diffusion runs low on persistence fast";
Talk: 13th IEEE International Conference on Computer Vision,
- 2011-11-13; in: "13th IEEE International Conference on Computer Vision",
Interpreting an image as a function on a compact subset of
the Euclidean plane, we get its scale-space by diffusion,
spreading the image over the entire plane. This generates
a 1-parameter family of functions alternatively deï¬ ned
as convolutions with a progressively wider Gaussian kernel.
We prove that the corresponding 1-parameter family of
persistence diagrams have norms that go rapidly to zero as
time goes to inï¬ nity. This result rationalizes experimental
observations about scale-space. We hope this will lead to
targeted improvements of related computer vision methods.
Created from the Publication Database of the Vienna University of Technology.