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Detail preservation of morphological operations through image scaling

Published: 29 March 2018 Publication History

Abstract

Morphological techniques probe an image with a structuring element. By varying the size and the shape of structuring elements, geometrical information of different parts of an image and their interrelation can be extracted for the applications of demodulating boundary, identifying components or removing noise. While large size elements benefits eliminating noise, they may be disadvantageous for preserving details in an image. Taking this into consideration, in this paper, we propose an image scaling method that will preserve detailed information when applying morphological operations to remove noise. First, a binary image is obtained, from which a Preservation Ratio Scalar (PRS) is calculated. The PRS is used for upscaling the image before morphological operations, which aims at preserving structural fine details otherwise eliminated in the original image. Finally, the morphological operator processed image is downscaled using the PRS. Experiments of target detection demonstrated the effectiveness of the proposed method in preserving the structural details such as edges while eliminating noises.

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Cited By

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  • (2018)Quantitative Analysis on Mathematical Morphology2018 12th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID)10.1109/ICASID.2018.8693120(103-106)Online publication date: Nov-2018

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cover image ACM Conferences
ACMSE '18: Proceedings of the 2018 ACM Southeast Conference
March 2018
246 pages
ISBN:9781450356961
DOI:10.1145/3190645
  • Conference Chair:
  • Ka-Wing Wong,
  • Program Chair:
  • Chi Shen,
  • Publications Chair:
  • Dana Brown
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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Association for Computing Machinery

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Publication History

Published: 29 March 2018

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Author Tags

  1. image scaling
  2. morphological image processing
  3. preservation ratio scale
  4. target detection

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  • Research-article

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ACM SE '18
Sponsor:
ACM SE '18: Southeast Conference
March 29 - 31, 2018
Kentucky, Richmond

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ACMSE '18 Paper Acceptance Rate 34 of 41 submissions, 83%;
Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2018)Quantitative Analysis on Mathematical Morphology2018 12th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID)10.1109/ICASID.2018.8693120(103-106)Online publication date: Nov-2018

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