A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da FEI |
Texto Completo: | https://repositorio.fei.edu.br/handle/FEI/1012 http://www.jmest.org/wp-content/uploads/JMESTN42352810.pdf |
Resumo: | An image segmentation process is one of the most important steps in an image recognition or analysis application pipeline. It is a step that splits each image into disjointed regions of interest. It is also a task that is usually performed by biological processes, such as human visual system. Due to the low processing and ease of implementation, one of the most used techniques is the thresholding method, which consists in finding the best cutting thresholds of a probability distribution histogram. However, the higher the number of thresholds, the greater the computational complexity. And there is no consensus on the number of thresholds and the partitioning position as well. This paper presents a study of the number of thresholds for segmenting an image into their regions of interest. For this purpose, the proposed method uses a bio-inspired algorithm based on meta-heuristics, called firefly with a non-extensive Tsallis statistics kernel. Also, the images are pre-filtered with a low-pass filter based on a q-gaussian function. Using a manually segmented database, the results show that there is an inverse correlation between the Fourier spectrum of an image and the number of thresholds which most approximates the image from the used ground truth. This suggests an automatic method for calculating the required number of thresholds. |
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RODRIGUES, PauloBOUZON, M. F.HORVATH, M.VARELA, V. P.LOPES, Guilherme2019-08-17T20:00:30Z2019-08-17T20:00:30Z2019RODRIGUES, Paulo; BOUZON, M. F.; HORVATH, M.; VARELA, V. P.; LOPES, Guilherme. A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics. Journal of Multidisciplinary Engineering Science and Technology, v. 6, n.1, p. 9411-9420, Jan. 2019.2458-9403https://repositorio.fei.edu.br/handle/FEI/1012http://www.jmest.org/wp-content/uploads/JMESTN42352810.pdfAn image segmentation process is one of the most important steps in an image recognition or analysis application pipeline. It is a step that splits each image into disjointed regions of interest. It is also a task that is usually performed by biological processes, such as human visual system. Due to the low processing and ease of implementation, one of the most used techniques is the thresholding method, which consists in finding the best cutting thresholds of a probability distribution histogram. However, the higher the number of thresholds, the greater the computational complexity. And there is no consensus on the number of thresholds and the partitioning position as well. This paper presents a study of the number of thresholds for segmenting an image into their regions of interest. For this purpose, the proposed method uses a bio-inspired algorithm based on meta-heuristics, called firefly with a non-extensive Tsallis statistics kernel. Also, the images are pre-filtered with a low-pass filter based on a q-gaussian function. Using a manually segmented database, the results show that there is an inverse correlation between the Fourier spectrum of an image and the number of thresholds which most approximates the image from the used ground truth. This suggests an automatic method for calculating the required number of thresholds.6194119420Journal of Multidisciplinary Engineering Science and TechnologyNon-extensive statistics,SIFTTsallis statisticsMulti-thresholding image segmentationFire-fly segmentationA study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statisticsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da FEIinstname:Centro Universitário da Fundação Educacional Inaciana (FEI)instacron:FEIORIGINALRI_1012.pdfRI_1012.pdfapplication/pdf1028783https://repositorio.fei.edu.br/bitstream/FEI/1012/1/RI_1012.pdf27872ffbb45bb058145bcdc2edddace3MD51TEXTRI_1012.pdf.txtRI_1012.pdf.txtExtracted texttext/plain47938https://repositorio.fei.edu.br/bitstream/FEI/1012/2/RI_1012.pdf.txt14737ba22a654f1415c1db702d3dbf7cMD52THUMBNAILRI_1012.pdf.jpgRI_1012.pdf.jpgGenerated Thumbnailimage/jpeg1885https://repositorio.fei.edu.br/bitstream/FEI/1012/3/RI_1012.pdf.jpg90d462fad38052d68eccc6dd4fab682dMD53FEI/10122019-10-22 00:00:44.949Biblioteca Digital de Teses e Dissertaçõeshttp://sofia.fei.edu.br/pergamum/biblioteca/PRI |
dc.title.pt_BR.fl_str_mv |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
title |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
spellingShingle |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics RODRIGUES, Paulo SIFT Tsallis statistics Multi-thresholding image segmentation Fire-fly segmentation Non-extensive statistics, |
title_short |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
title_full |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
title_fullStr |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
title_full_unstemmed |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
title_sort |
A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics |
author |
RODRIGUES, Paulo |
author_facet |
RODRIGUES, Paulo BOUZON, M. F. HORVATH, M. VARELA, V. P. LOPES, Guilherme |
author_role |
author |
author2 |
BOUZON, M. F. HORVATH, M. VARELA, V. P. LOPES, Guilherme |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
RODRIGUES, Paulo BOUZON, M. F. HORVATH, M. VARELA, V. P. LOPES, Guilherme |
dc.subject.eng.fl_str_mv |
SIFT Tsallis statistics Multi-thresholding image segmentation Fire-fly segmentation |
topic |
SIFT Tsallis statistics Multi-thresholding image segmentation Fire-fly segmentation Non-extensive statistics, |
dc.subject.other.en.fl_str_mv |
Non-extensive statistics, |
description |
An image segmentation process is one of the most important steps in an image recognition or analysis application pipeline. It is a step that splits each image into disjointed regions of interest. It is also a task that is usually performed by biological processes, such as human visual system. Due to the low processing and ease of implementation, one of the most used techniques is the thresholding method, which consists in finding the best cutting thresholds of a probability distribution histogram. However, the higher the number of thresholds, the greater the computational complexity. And there is no consensus on the number of thresholds and the partitioning position as well. This paper presents a study of the number of thresholds for segmenting an image into their regions of interest. For this purpose, the proposed method uses a bio-inspired algorithm based on meta-heuristics, called firefly with a non-extensive Tsallis statistics kernel. Also, the images are pre-filtered with a low-pass filter based on a q-gaussian function. Using a manually segmented database, the results show that there is an inverse correlation between the Fourier spectrum of an image and the number of thresholds which most approximates the image from the used ground truth. This suggests an automatic method for calculating the required number of thresholds. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-08-17T20:00:30Z |
dc.date.available.fl_str_mv |
2019-08-17T20:00:30Z |
dc.date.issued.fl_str_mv |
2019 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
RODRIGUES, Paulo; BOUZON, M. F.; HORVATH, M.; VARELA, V. P.; LOPES, Guilherme. A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics. Journal of Multidisciplinary Engineering Science and Technology, v. 6, n.1, p. 9411-9420, Jan. 2019. |
dc.identifier.uri.fl_str_mv |
https://repositorio.fei.edu.br/handle/FEI/1012 |
dc.identifier.issn.none.fl_str_mv |
2458-9403 |
dc.identifier.url.none.fl_str_mv |
http://www.jmest.org/wp-content/uploads/JMESTN42352810.pdf |
identifier_str_mv |
RODRIGUES, Paulo; BOUZON, M. F.; HORVATH, M.; VARELA, V. P.; LOPES, Guilherme. A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics. Journal of Multidisciplinary Engineering Science and Technology, v. 6, n.1, p. 9411-9420, Jan. 2019. 2458-9403 |
url |
https://repositorio.fei.edu.br/handle/FEI/1012 http://www.jmest.org/wp-content/uploads/JMESTN42352810.pdf |
dc.relation.ispartof.none.fl_str_mv |
Journal of Multidisciplinary Engineering Science and Technology |
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openAccess |
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