Noise estimation of a local energy image
Phase congruency is a relatively unknown and powerful image processing technique for segmentation, which has been used in image processing. However, a limitation of this technique is its sensitivity to noise. Therefore, to prevent that noise affects segmentation results, it is necessary a good estim...
- Autores:
-
Cifuentes, Tatiana Hernández
Aroca, Yorladys Martínez
Jamioy, Carlos Antonio Jacanamejoy
Vargas, Manuel Guillermo Forero Send mail to Vargas M.G.F.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5828
- Acceso en línea:
- https://doi.org/10.15446/recolma.v57n2.115855
https://hdl.handle.net/20.500.12313/5828
http://revistas.unal.edu.co/index.php/recolma/article/view/115855
- Palabra clave:
- Computational methods
Image processing
Point estimation
- Rights
- openAccess
- License
- © 2023 Universidad Nacional de Colombia. All rights reserved.
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Noise estimation of a local energy image |
| dc.title.translated.none.fl_str_mv |
Estimación del modelo de ruido de una imagen de energía local utilizando la distribución Weibull |
| title |
Noise estimation of a local energy image |
| spellingShingle |
Noise estimation of a local energy image Computational methods Image processing Point estimation |
| title_short |
Noise estimation of a local energy image |
| title_full |
Noise estimation of a local energy image |
| title_fullStr |
Noise estimation of a local energy image |
| title_full_unstemmed |
Noise estimation of a local energy image |
| title_sort |
Noise estimation of a local energy image |
| dc.creator.fl_str_mv |
Cifuentes, Tatiana Hernández Aroca, Yorladys Martínez Jamioy, Carlos Antonio Jacanamejoy Vargas, Manuel Guillermo Forero Send mail to Vargas M.G.F. |
| dc.contributor.author.none.fl_str_mv |
Cifuentes, Tatiana Hernández Aroca, Yorladys Martínez Jamioy, Carlos Antonio Jacanamejoy Vargas, Manuel Guillermo Forero Send mail to Vargas M.G.F. |
| dc.subject.proposal.eng.fl_str_mv |
Computational methods Image processing Point estimation |
| topic |
Computational methods Image processing Point estimation |
| description |
Phase congruency is a relatively unknown and powerful image processing technique for segmentation, which has been used in image processing. However, a limitation of this technique is its sensitivity to noise. Therefore, to prevent that noise affects segmentation results, it is necessary a good estimation of its level, considering that in phase congruency, this estimation is based on the local energy image. Consequently, to improve the results of this technique, it is essential to perform a good detection of the noise threshold. In this work, we introduce an efficient method to estimate parameters of a Weibull distribution which is used to modelate the noise of energy image in phase congruency. |
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2023 |
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2023 |
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2025-10-24T14:27:35Z |
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2025-10-24T14:27:35Z |
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Artículo de revista |
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Hernandez Cifuentes, T., Martinez Aroca, Y., Jacanamejoy Jamioy, C. A. & Forero Vargas, M. G. (2024). Estimación del modelo de ruido de una imagen de energía local utilizando la distribución Weibull. Revista Colombiana de Matemáticas, 57(2), 207–219. https://doi.org/10.15446/recolma.v57n2.115855 |
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https://doi.org/10.15446/recolma.v57n2.115855 |
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23574100 |
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00347426 |
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https://hdl.handle.net/20.500.12313/5828 |
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http://revistas.unal.edu.co/index.php/recolma/article/view/115855 |
| identifier_str_mv |
Hernandez Cifuentes, T., Martinez Aroca, Y., Jacanamejoy Jamioy, C. A. & Forero Vargas, M. G. (2024). Estimación del modelo de ruido de una imagen de energía local utilizando la distribución Weibull. Revista Colombiana de Matemáticas, 57(2), 207–219. https://doi.org/10.15446/recolma.v57n2.115855 23574100 00347426 |
| url |
https://doi.org/10.15446/recolma.v57n2.115855 https://hdl.handle.net/20.500.12313/5828 http://revistas.unal.edu.co/index.php/recolma/article/view/115855 |
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eng |
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eng |
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219 |
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207 |
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57 |
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Revista Colombiana de Matematicas |
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I.E. Abdou and W.K. Pratt,Quantitative design and evaluation of enhan-cement/thresholding edge detectors, Proceedings of the IEEE67(1979),no. 5, 753–763. I Ben Ayed, Nacera Hennane, and Amar Mitiche,Unsupervised variationalimage segmentation/classification using a weibull observation model, IEEETransactions on Image Processing15(2006), no. 11, 3431–3439. J Constante, A Cuesta, and D Jij ́on,Fitting methods of two-parameterweibull of wind series and electric-wind potential estimation m ́etodos deajuste de weibull de dos par ́ametros en series de viento y estimaci ́on delpotencial eolo-el ́ectrico, Arenal1(2021), no. 78,889, 78–889. Lee R Dice,Measures of the amount of ecologic association between species,Ecology26(1945), no. 3, 297–302. Manuel G. Forero and Carlos A. Jacanamejoy,Unified mathematical for-mulation of monogenic phase congruency, Mathematics9(2021), no. 23,3080 M Ganji, H Bevrani, N Hami Golzar, and S Zabihi,The weibull-rayleighdistribution, some properties, and applications., Journal of MathematicalSciences218(2016), no. 3. Jan-Mark Geusebroek and Arnold WM Smeulders,Fragmentation in thevision of scenes, null, IEEE, 2003, p. 130. Jan-Mark Geusebroek, Arnold WM Smeulders, et al.,A physical expla-nation for natural image statistics, Proceedings of the 2nd InternationalWorkshop on Texture Analysis and Synthesis (Texture 2002), Heriot-WattUniversity, 2002, pp. 47–52. Carlos Jacanamejoy, Nohora Meneses-Casas, and Manuel G Forero,Imagefeature detection based on phase congruency by monogenic filters with newnoise estimation, Iberian Conference on Pattern Recognition and ImageAnalysis, Springer, 2019, pp. 577–588. Carlos A. Jacanamejoy and Manuel G. Forero,A note on the phase con-gruence method in image analysis, Iberoamerican Congress on PatternRecognition, Springer, 2018, pp. 384–391 Peter Kovesi,Image features from phase congruency, Videre: Journal ofcomputer vision research1(1999), no. 3, 1–26. ___________,Matlab and octave functions for computer vision and image pro-cessing,Available at http://www.peterkovesi.com/matlabfns/#phasecong,2013 Max Mignotte, Christophe Collet, Patrick Perez, and Patrick Bouthemy,Sonar image segmentation using an unsupervised hierarchical mrf model,IEEE transactions on image processing9(2000), no. 7, 1216–1231 Douglas C Montgomery and George C Runger,Applied statistics and pro-bability for engineers, John Wiley & Sons, 2010 M Concetta Morrone and Robyn A Owens,Feature detection from localenergy, Pattern recognition letters6(1987), no. 5, 303–313 Lord Rayleigh,Xii. on the resultant of a large number of vibrations of thesame pitch and of arbitrary phase, The London, Edinburgh, and DublinPhilosophical Magazine and Journal of Science10(1880), no. 60, 73–78 H Steven Scholte, Sennay Ghebreab, Lourens Waldorp, Arnold WMSmeulders, and Victor AF Lamme,Brain responses strongly correlate withweibull image statistics when processing natural images, Journal of Vision9(2009), no. 4, 29–29 Heidi M Sosik and Robert J Olson,Automated taxonomic classificationof phytoplankton sampled with imaging-in-flow cytometry, Limnology andOceanography: Methods5(2007), no. 6, 204–216. S Venkatesh and R Owens,An energy feature detection scheme, ICIP’89:IEEE International Conference on Image Processing: conference procee-dings, 5-8 September 1989, Singapore, IEEE, 1989 Tjalling J Ypma,Historical development of the newton–raphson method,SIAM review37(1995), no. 4, 531–551 |
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© 2023 Universidad Nacional de Colombia. All rights reserved. |
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Cifuentes, Tatiana Hernándezb2fd0734-6fd1-48d1-8307-f92c6dcea64e-1Aroca, Yorladys Martínez86341488-88f0-41e3-926b-b3a0129af6b8-1Jamioy, Carlos Antonio Jacanamejoyd8cfe5c3-6eee-4671-a21e-d87f8bfedc3b-1Vargas, Manuel Guillermo Forero Send mail to Vargas M.G.F.cca461a2-8260-4c27-9dd6-ea04182c537e-12025-10-24T14:27:35Z2025-10-24T14:27:35Z2023Phase congruency is a relatively unknown and powerful image processing technique for segmentation, which has been used in image processing. However, a limitation of this technique is its sensitivity to noise. Therefore, to prevent that noise affects segmentation results, it is necessary a good estimation of its level, considering that in phase congruency, this estimation is based on the local energy image. Consequently, to improve the results of this technique, it is essential to perform a good detection of the noise threshold. In this work, we introduce an efficient method to estimate parameters of a Weibull distribution which is used to modelate the noise of energy image in phase congruency.application/pdfHernandez Cifuentes, T., Martinez Aroca, Y., Jacanamejoy Jamioy, C. A. & Forero Vargas, M. G. (2024). Estimación del modelo de ruido de una imagen de energía local utilizando la distribución Weibull. Revista Colombiana de Matemáticas, 57(2), 207–219. https://doi.org/10.15446/recolma.v57n2.115855https://doi.org/10.15446/recolma.v57n2.1158552357410000347426https://hdl.handle.net/20.500.12313/5828http://revistas.unal.edu.co/index.php/recolma/article/view/115855engUniversidad Nacional de ColombiaColombia219220757Revista Colombiana de MatematicasI.E. Abdou and W.K. Pratt,Quantitative design and evaluation of enhan-cement/thresholding edge detectors, Proceedings of the IEEE67(1979),no. 5, 753–763.I Ben Ayed, Nacera Hennane, and Amar Mitiche,Unsupervised variationalimage segmentation/classification using a weibull observation model, IEEETransactions on Image Processing15(2006), no. 11, 3431–3439.J Constante, A Cuesta, and D Jij ́on,Fitting methods of two-parameterweibull of wind series and electric-wind potential estimation m ́etodos deajuste de weibull de dos par ́ametros en series de viento y estimaci ́on delpotencial eolo-el ́ectrico, Arenal1(2021), no. 78,889, 78–889.Lee R Dice,Measures of the amount of ecologic association between species,Ecology26(1945), no. 3, 297–302.Manuel G. Forero and Carlos A. Jacanamejoy,Unified mathematical for-mulation of monogenic phase congruency, Mathematics9(2021), no. 23,3080M Ganji, H Bevrani, N Hami Golzar, and S Zabihi,The weibull-rayleighdistribution, some properties, and applications., Journal of MathematicalSciences218(2016), no. 3.Jan-Mark Geusebroek and Arnold WM Smeulders,Fragmentation in thevision of scenes, null, IEEE, 2003, p. 130.Jan-Mark Geusebroek, Arnold WM Smeulders, et al.,A physical expla-nation for natural image statistics, Proceedings of the 2nd InternationalWorkshop on Texture Analysis and Synthesis (Texture 2002), Heriot-WattUniversity, 2002, pp. 47–52.Carlos Jacanamejoy, Nohora Meneses-Casas, and Manuel G Forero,Imagefeature detection based on phase congruency by monogenic filters with newnoise estimation, Iberian Conference on Pattern Recognition and ImageAnalysis, Springer, 2019, pp. 577–588.Carlos A. Jacanamejoy and Manuel G. Forero,A note on the phase con-gruence method in image analysis, Iberoamerican Congress on PatternRecognition, Springer, 2018, pp. 384–391Peter Kovesi,Image features from phase congruency, Videre: Journal ofcomputer vision research1(1999), no. 3, 1–26.___________,Matlab and octave functions for computer vision and image pro-cessing,Available at http://www.peterkovesi.com/matlabfns/#phasecong,2013Max Mignotte, Christophe Collet, Patrick Perez, and Patrick Bouthemy,Sonar image segmentation using an unsupervised hierarchical mrf model,IEEE transactions on image processing9(2000), no. 7, 1216–1231Douglas C Montgomery and George C Runger,Applied statistics and pro-bability for engineers, John Wiley & Sons, 2010M Concetta Morrone and Robyn A Owens,Feature detection from localenergy, Pattern recognition letters6(1987), no. 5, 303–313Lord Rayleigh,Xii. on the resultant of a large number of vibrations of thesame pitch and of arbitrary phase, The London, Edinburgh, and DublinPhilosophical Magazine and Journal of Science10(1880), no. 60, 73–78H Steven Scholte, Sennay Ghebreab, Lourens Waldorp, Arnold WMSmeulders, and Victor AF Lamme,Brain responses strongly correlate withweibull image statistics when processing natural images, Journal of Vision9(2009), no. 4, 29–29Heidi M Sosik and Robert J Olson,Automated taxonomic classificationof phytoplankton sampled with imaging-in-flow cytometry, Limnology andOceanography: Methods5(2007), no. 6, 204–216.S Venkatesh and R Owens,An energy feature detection scheme, ICIP’89:IEEE International Conference on Image Processing: conference procee-dings, 5-8 September 1989, Singapore, IEEE, 1989Tjalling J Ypma,Historical development of the newton–raphson method,SIAM review37(1995), no. 4, 531–551© 2023 Universidad Nacional de Colombia. All rights reserved.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Computational methodsImage processingPoint estimationNoise estimation of a local energy imageEstimación del modelo de ruido de una imagen de energía local utilizando la distribución WeibullArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationTEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain2https://repositorio.unibague.edu.co/bitstreams/f5330cbe-f644-4d8f-b3d9-e94659486f4e/downloade1c06d85ae7b8b032bef47e42e4c08f9MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg15428https://repositorio.unibague.edu.co/bitstreams/c4bb7397-679c-4952-81e5-2f6b93e6fb28/download4b8d5c90aaa3f6c5cc3b8953817ef271MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/c5ee5511-5ca7-4f1c-96dc-23910e263244/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf195741https://repositorio.unibague.edu.co/bitstreams/9c8e4327-e0cb-463d-a98c-6fc4c58b4f52/downloada35059660c40635d8e00a6a5ecd0c0eeMD5220.500.12313/5828oai:repositorio.unibague.edu.co:20.500.12313/58282025-10-25 03:01:39.469https://creativecommons.org/licenses/by/4.0/© 2023 Universidad Nacional de Colombia. 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