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Automated Fabric Defect Detection Using à trous Wavelet Transform and Bollinger Band (BB)

Published: 05 January 2021 Publication History

Abstract

This paper introduces a novel fabric defect detection technique based on wavelet transform and Bollinger Band (BB) estimation for thresholding. In this paper, à trous wavelet is utilized to extract the approximate sub-image at the first decomposition level. In the approximate sub-image, the energy of the defective region is enhanced, however the energy of the background is attenuated. Then, we estimate the standared deviations of BBs to detect the defected area in the fabric. This method gives detection results up to 100%.

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Images database: Industrial Automation Research Laboratory, Dept. of Electrical and Electronic Engineering, The University of Hong Kong

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  1. Automated Fabric Defect Detection Using à trous Wavelet Transform and Bollinger Band (BB)

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    ICSIE '20: Proceedings of the 9th International Conference on Software and Information Engineering
    November 2020
    251 pages
    ISBN:9781450377218
    DOI:10.1145/3436829
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Ain Shams University: Ain Shams University, Egypt

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    Published: 05 January 2021

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    Author Tags

    1. à trous wavelet
    2. Bollinger Bands
    3. Fabric defect detection
    4. Wavelets

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