Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/36211 |
Resumo: | Modernization is present in all fields of knowledge. The wavelet transform and the Hurst exponent are tools that have fundamental importance in many of these advances. In the present study, the wavelet decomposition technique was combined with the Hurst exponent calculation to analyze X-ray images of seeds and thus classify them as full, slightly damaged or damaged. To calculate the Hurst exponent the mean and median were used as measurements of position. A support vector machine was used to validate the proposed method. For the full, damaged and slightly damaged seed groups, the average accuracy of the method, using the mean as measure position, was 74.5%, and using the median was 57.05%. For the full and damaged seed groups, the average accuracy using the mean was 99.76%, and using the median was 80.93%. For the slightly damaged and damaged seed groups, the average accuracy, using the mean as measure of position, was 99.26%, and the median was 76.22%. When analyzing seeds with slight damage, we observed a decrease in accuracy because the classification of the X-rays was subjective. Therefore, for the image database used in this study, the proposed methodology is efficient for automatic classification. |
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Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent Exponente direccional de Hurst en el análisis de similitud de imágenes de semillasExpoente direcional de Hurst na análise de similaridade de imagens de sementes AutossimilaridadeClassificação de imagensOndaletas.Auto-similitudClasificación de imágenesOndiculas.AutossimilarityImage ClassificationWavelets.Modernization is present in all fields of knowledge. The wavelet transform and the Hurst exponent are tools that have fundamental importance in many of these advances. In the present study, the wavelet decomposition technique was combined with the Hurst exponent calculation to analyze X-ray images of seeds and thus classify them as full, slightly damaged or damaged. To calculate the Hurst exponent the mean and median were used as measurements of position. A support vector machine was used to validate the proposed method. For the full, damaged and slightly damaged seed groups, the average accuracy of the method, using the mean as measure position, was 74.5%, and using the median was 57.05%. For the full and damaged seed groups, the average accuracy using the mean was 99.76%, and using the median was 80.93%. For the slightly damaged and damaged seed groups, the average accuracy, using the mean as measure of position, was 99.26%, and the median was 76.22%. When analyzing seeds with slight damage, we observed a decrease in accuracy because the classification of the X-rays was subjective. Therefore, for the image database used in this study, the proposed methodology is efficient for automatic classification.La modernización está presente en todos los campos del conocimiento. Técnicas más sofisticadas y aparatos más modernos están surgiendo constantemente. La descomposición en ondiculas es una herramienta que tiene una importancia fundamental en muchos de estos avances. En lo que se refiere al análisis de imágenes, esta herramienta ha contribuido para creación de nuevas técnicas diferentes, como para la reconstrucción, comprensión y eliminación de ruidos, entre otros. Otra herramienta que auxilia en el análisis de imágenes es el exponente de Hurst, que mide cuanto tiene de auto-similitud una imagen, de forma que se capte información sobre las características de la imagen que a simple vista no seria posible. Con ello, el objetivo de este trabajo será combinar la técnica de descomposición en ondiculas con el cálculo del exponente de Hurst para analizar imágenes de semillas y así poder clasificarlas en llenas, levemente dañadas o dañadas. En el cálculo del exponente de Hurst serán usadas como medida de localización la media y la mediana. Un modelo de Máquinas de Vectores de Soporte será usado para la validación del método propuesto. Para el grupo de todas las semillas la precisión media del método, utilizando la media, fue de 74,5% y, con la mediana fue de 57,05%. Utilizando el grupo de semillas llenas y dañadas, la tasa media de precisión, con la media como medida de posición, fue de 99,76% y, con la mediana fue de 80,93%. En el grupo que contiene semillas levemente dañadas y dañadas la tasa media de precisión, usando la media como medida de posición, fue de 99,26% y, con la mediana fue de 76,22%.A modernização está presente em todos os campos do conhecimento. Técnicas mais sofisticadas e aparelhos mais modernos têm surgido com frequência. A decomposição em ondaletas é uma ferramenta que tem fundamental importância em muitos desses avanços. No que se refere à análise de imagens, essa ferramenta tem cooperado para criação de diversas novas técnicas, seja para reconstrução, compressão, eliminação de ruído, dentre outros. Outra ferramenta que auxilia na análise de imagens é o expoente de Hurst, que mede o quão autossimilar uma imagem é, de forma que se capte informação sobre características da imagem que a olho nu não seria possível. Com isso, o objetivo deste trabalho será combinar a técnica de decomposição em ondaletas com o cálculo do expoente de Hurst para analisar imagens de sementes e assim classificá-las em cheias, levemente danificadas ou danificadas. No cálculo do expoente de Hurst serão usadas como medidas de localização a média e a mediana. Um suport vector machine foi usado para validação do método proposto. Para o grupo de todas as sementes a acurácia média do método, utilizando a média, foi de 74,5% e, para a mediana foi de 57,05%. Utilizando o grupo de sementes cheias e danificadas a taxa média de acurácia, com a média como medida de posição, foi de 99,76% e, com a mediana foi de 80,93%. No grupo contendo sementes levemente danificadas e danificadas a taxa média de acurácia, usando a média como medida de posição, foi de 99,26% e, com a mediana foi de 76,22%.Research, Society and Development2022-10-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/3621110.33448/rsd-v11i14.36211Research, Society and Development; Vol. 11 No. 14; e297111436211Research, Society and Development; Vol. 11 Núm. 14; e297111436211Research, Society and Development; v. 11 n. 14; e2971114362112525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/36211/30359Copyright (c) 2022 Fernando Ribeiro Cassiano; Thelma Sáfadi; Paulo Henrique Sales Guimarãeshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCassiano, Fernando Ribeiro Sáfadi, Thelma Guimarães, Paulo Henrique Sales 2022-11-08T13:36:27Zoai:ojs.pkp.sfu.ca:article/36211Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:50:50.750957Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent Exponente direccional de Hurst en el análisis de similitud de imágenes de semillas Expoente direcional de Hurst na análise de similaridade de imagens de sementes |
title |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
spellingShingle |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent Cassiano, Fernando Ribeiro Autossimilaridade Classificação de imagens Ondaletas. Auto-similitud Clasificación de imágenes Ondiculas. Autossimilarity Image Classification Wavelets. |
title_short |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
title_full |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
title_fullStr |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
title_full_unstemmed |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
title_sort |
Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent |
author |
Cassiano, Fernando Ribeiro |
author_facet |
Cassiano, Fernando Ribeiro Sáfadi, Thelma Guimarães, Paulo Henrique Sales |
author_role |
author |
author2 |
Sáfadi, Thelma Guimarães, Paulo Henrique Sales |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Cassiano, Fernando Ribeiro Sáfadi, Thelma Guimarães, Paulo Henrique Sales |
dc.subject.por.fl_str_mv |
Autossimilaridade Classificação de imagens Ondaletas. Auto-similitud Clasificación de imágenes Ondiculas. Autossimilarity Image Classification Wavelets. |
topic |
Autossimilaridade Classificação de imagens Ondaletas. Auto-similitud Clasificación de imágenes Ondiculas. Autossimilarity Image Classification Wavelets. |
description |
Modernization is present in all fields of knowledge. The wavelet transform and the Hurst exponent are tools that have fundamental importance in many of these advances. In the present study, the wavelet decomposition technique was combined with the Hurst exponent calculation to analyze X-ray images of seeds and thus classify them as full, slightly damaged or damaged. To calculate the Hurst exponent the mean and median were used as measurements of position. A support vector machine was used to validate the proposed method. For the full, damaged and slightly damaged seed groups, the average accuracy of the method, using the mean as measure position, was 74.5%, and using the median was 57.05%. For the full and damaged seed groups, the average accuracy using the mean was 99.76%, and using the median was 80.93%. For the slightly damaged and damaged seed groups, the average accuracy, using the mean as measure of position, was 99.26%, and the median was 76.22%. When analyzing seeds with slight damage, we observed a decrease in accuracy because the classification of the X-rays was subjective. Therefore, for the image database used in this study, the proposed methodology is efficient for automatic classification. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-27 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/36211 10.33448/rsd-v11i14.36211 |
url |
https://rsdjournal.org/index.php/rsd/article/view/36211 |
identifier_str_mv |
10.33448/rsd-v11i14.36211 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/36211/30359 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Fernando Ribeiro Cassiano; Thelma Sáfadi; Paulo Henrique Sales Guimarães https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Fernando Ribeiro Cassiano; Thelma Sáfadi; Paulo Henrique Sales Guimarães https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 14; e297111436211 Research, Society and Development; Vol. 11 Núm. 14; e297111436211 Research, Society and Development; v. 11 n. 14; e297111436211 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
_version_ |
1797052726599745536 |