# Fast Convolutional Distance Transform

Christina Karam*, Kenjiro Sugimoto, Keigo Hirakawa

*この研究の対応する著者

5 被引用数 (Scopus)

## 抄録

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.

本文言語 English 8686167 853-857 5 IEEE Signal Processing Letters 26 6 https://doi.org/10.1109/LSP.2019.2910466 Published - 2019 6月

• 信号処理
• 電子工学および電気工学
• 応用数学

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