English

Local, Smooth, and Consistent Jacobi Set Simplification

Computational Geometry 2013-07-31 v1 Data Structures and Algorithms

Abstract

The relation between two Morse functions defined on a common domain can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces have shown to be useful in various applications. Unfortunately, in practice functions often contain noise and discretization artifacts causing their Jacobi set to become unmanageably large and complex. While there exist techniques to simplify Jacobi sets, these are unsuitable for most applications as they lack fine-grained control over the process and heavily restrict the type of simplifications possible. In this paper, we introduce a new framework that generalizes critical point cancellations in scalar functions to Jacobi sets in two dimensions. We focus on simplifications that can be realized by smooth approximations of the corresponding functions and show how this implies simultaneously simplifying contiguous subsets of the Jacobi set. These extended cancellations form the atomic operations in our framework, and we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications according to some user-defined metric. We prove that the algorithm is correct and terminates only once no more local, smooth and consistent simplifications are possible. We disprove a previous claim on the minimal Jacobi set for manifolds with arbitrary genus and show that for simply connected domains, our algorithm reduces a given Jacobi set to its simplest configuration.

Keywords

Cite

@article{arxiv.1307.7752,
  title  = {Local, Smooth, and Consistent Jacobi Set Simplification},
  author = {Harsh Bhatia and Bei Wang and Gregory Norgard and Valerio Pascucci and Peer-Timo Bremer},
  journal= {arXiv preprint arXiv:1307.7752},
  year   = {2013}
}

Comments

24 pages, 19 figures

R2 v1 2026-06-22T00:59:55.411Z