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A Two-Stage Automatic Color Thresholding Technique

Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity o...

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Detalles Bibliográficos
Autores principales: Pootheri, Shamna, Ellam, Daniel, Grübl, Thomas, Liu, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059933/
https://www.ncbi.nlm.nih.gov/pubmed/36992072
http://dx.doi.org/10.3390/s23063361
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author Pootheri, Shamna
Ellam, Daniel
Grübl, Thomas
Liu, Yang
author_facet Pootheri, Shamna
Ellam, Daniel
Grübl, Thomas
Liu, Yang
author_sort Pootheri, Shamna
collection PubMed
description Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background–foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.
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spelling pubmed-100599332023-03-30 A Two-Stage Automatic Color Thresholding Technique Pootheri, Shamna Ellam, Daniel Grübl, Thomas Liu, Yang Sensors (Basel) Article Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background–foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve. MDPI 2023-03-22 /pmc/articles/PMC10059933/ /pubmed/36992072 http://dx.doi.org/10.3390/s23063361 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pootheri, Shamna
Ellam, Daniel
Grübl, Thomas
Liu, Yang
A Two-Stage Automatic Color Thresholding Technique
title A Two-Stage Automatic Color Thresholding Technique
title_full A Two-Stage Automatic Color Thresholding Technique
title_fullStr A Two-Stage Automatic Color Thresholding Technique
title_full_unstemmed A Two-Stage Automatic Color Thresholding Technique
title_short A Two-Stage Automatic Color Thresholding Technique
title_sort two-stage automatic color thresholding technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059933/
https://www.ncbi.nlm.nih.gov/pubmed/36992072
http://dx.doi.org/10.3390/s23063361
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