TY - CONF
AB - Interpreting an image as a function on a compact sub- set of the Euclidean plane, we get its scale-space by diffu- sion, spreading the image over the entire plane. This gener- ates a 1-parameter family of functions alternatively defined as convolutions with a progressively wider Gaussian ker- nel. We prove that the corresponding 1-parameter family of persistence diagrams have norms that go rapidly to zero as time goes to infinity. This result rationalizes experimental observations about scale-space. We hope this will lead to targeted improvements of related computer vision methods.
AU - Chen, Chao
AU - Edelsbrunner, Herbert
ID - 3313
T2 - Proceedings of the IEEE International Conference on Computer Vision
TI - Diffusion runs low on persistence fast
ER -