Fast Convolutional Distance Transform

Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We propose 'convolutional distance transform' - efficient implementations of distance transform. Specifically, we leverage approximate minimum functions to rewrite the distance transform in terms of convolution operators. Thanks to the fast Fourier transform, the proposed convolutional distance transforms have \mathcal {O}(N\log N) complexity, where N is the total number of pixels. The proposed acceleration technique is 'distance metric agnostic.' In the special case that the distance function is a p-norm, the distance transform can be further reduced to separable convolution filters; and for Euclidean norm, we achieve \mathcal {O}(N) using constant-time Gaussian filtering.

Original languageEnglish
Article number8686167
Pages (from-to)853-857
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number6
DOIs
Publication statusPublished - 2019 Jun 1
Externally publishedYes

Keywords

  • Convolution
  • distance transform

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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