Accelerating the Distribution Estimation for the Weighted Median/Mode Filters
Refereed conference paper presented and published in conference proceedings


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AbstractVarious image filters for applications in the area of computer vision require the properties of the local statistics of the input image, which are always defined by the local distribution or histogram. But the huge expense of computing the distribution hampers the popularity of these filters in real-time or interactive-rate systems. In this paper, we present an efficient and practical method to estimate the local weighted distribution for the weighted median/mode filters based on the kernel density estimation with a new separable kernel defined by a weighted combinations of a series of probabilistic generative models. It reduces the large number of filtering operations in previous constant time algorithms [1,2] to a small amount, which is also adaptive to the structure of the input image. The proposed accelerated weighted median/mode filters are effective and efficient for a variety of applications, which have comparable performance against the current state-of-the-art counterparts and cost only a fraction of their execution time.
All Author(s) ListSheng L, Ngan KN, Hui TW
Name of Conference12th Asian Conference on Computer Vision (ACCV)
Start Date of Conference01/11/2014
End Date of Conference05/11/2014
Place of ConferenceSingapore
Country/Region of ConferenceSingapore
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptionorganized by Asian Federation of Computer Vision Societies,
Year2015
Month1
Day1
Volume Number9006
PublisherSPRINGER-VERLAG BERLIN
Pages3 - 17
ISBN978-3-319-16816-6
eISBN978-3-319-16817-3
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Medical Informatics; Robotics

Last updated on 2020-28-05 at 02:46