Clustering-based Acceleration for High-dimensional Gaussian Filtering

Sou Oishi, Norishige Fukushima

2021

Abstract

Edge-preserving filtering is an essential tool for image processing applications and has various types of filtering. For real-time applications, acceleration of its speed is also essential. To accelerate various types of edge-preserving filtering, we represent various edge-preserving filtering by high-dimensional Gaussian filtering. Then, we accelerate the high-dimensional Gaussian filtering by clustering-based constant algorithm, which has O(K) order, where K is the number of clusters. The clustering-based method was developed for color bilateral filtering; however, this paper used it for high-dimensional bilateral filtering. Also, cooperating with tiling, k-means++, and principal component analysis, we can further improve the filter’s performance. Experimental results show that our method can approximate various edge-preserving filtering by approximated clustering-based high-dimensional Gaussian filtering.

Paper Citation

in Harvard Style

Oishi S. and Fukushima N. (2021). Clustering-based Acceleration for High-dimensional Gaussian Filtering. In Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, ISBN 978-989-758-525-8, pages 65-72. DOI: 10.5220/0010548600650072

in Bibtex Style

@conference{sigmap21,
author={Sou Oishi and Norishige Fukushima},
title={Clustering-based Acceleration for High-dimensional Gaussian Filtering},
booktitle={Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP,},
year={2021},
pages={65-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010548600650072},
isbn={978-989-758-525-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP,
TI - Clustering-based Acceleration for High-dimensional Gaussian Filtering
SN - 978-989-758-525-8
AU - Oishi S.
AU - Fukushima N.
PY - 2021
SP - 65
EP - 72
DO - 10.5220/0010548600650072