An efficient unsupervised image quality metric with application for condition recognition in kiln-Reference-Cited by-同舟云学术

An efficient unsupervised image quality metric with application for condition recognition in kiln

Published:2022-01 Issue: Volume:107 Page:104547
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Wu Leyuan^ORCID,Zhang Xiaogang,Chen Hua,Zhou Yicong,Wang Lianhong,Wang Dingxiang

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference37 articles.

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2. Recognition of the temperature condition of a rotary kiln using dynamic features of a series of blurry flame images;Chen;IEEE Trans. Ind. Inf.,2016

3. No-reference quality assessment of contrast-distorted images based on natural scene statistics;Fang;IEEE Signal Process. Lett.,2015

4. Image quality assessment for advertising applications based on neural network;Fong;J. Vis. Commun. Image Represent.,2019

5. The analysis of image contrast: From quality assessment to automatic enhancement;Gu;IEEE Trans. Cybern.,2016

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