Generalized Quantification Function of Monogenic Phase Congruency

Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, o...

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Autores:
Forero, Manuel G.
Jacanamejoy, Carlos A
Machado, Maximiliano
Penagos, Karla L.
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:20.500.12313/5555
Acceso en línea:
https://hdl.handle.net/20.500.12313/5555
https://www.mdpi.com/2227-7390/11/17/3795
Palabra clave:
Fase Monogénica
Detección de bordes
Transformada de Fourier
Energía local
Filtro log-Gabor
Filtros monogénicos
Congruencia de fase
Edge detection
Fourier transform
Local energy
Log-Gabor filter
Monogenic filters
Phase congruency
Rights
openAccess
License
© 2023 by the authors.
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network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.eng.fl_str_mv Generalized Quantification Function of Monogenic Phase Congruency
title Generalized Quantification Function of Monogenic Phase Congruency
spellingShingle Generalized Quantification Function of Monogenic Phase Congruency
Fase Monogénica
Detección de bordes
Transformada de Fourier
Energía local
Filtro log-Gabor
Filtros monogénicos
Congruencia de fase
Edge detection
Fourier transform
Local energy
Log-Gabor filter
Monogenic filters
Phase congruency
title_short Generalized Quantification Function of Monogenic Phase Congruency
title_full Generalized Quantification Function of Monogenic Phase Congruency
title_fullStr Generalized Quantification Function of Monogenic Phase Congruency
title_full_unstemmed Generalized Quantification Function of Monogenic Phase Congruency
title_sort Generalized Quantification Function of Monogenic Phase Congruency
dc.creator.fl_str_mv Forero, Manuel G.
Jacanamejoy, Carlos A
Machado, Maximiliano
Penagos, Karla L.
dc.contributor.author.none.fl_str_mv Forero, Manuel G.
Jacanamejoy, Carlos A
Machado, Maximiliano
Penagos, Karla L.
dc.subject.armarc.none.fl_str_mv Fase Monogénica
Detección de bordes
Transformada de Fourier
Energía local
Filtro log-Gabor
Filtros monogénicos
Congruencia de fase
topic Fase Monogénica
Detección de bordes
Transformada de Fourier
Energía local
Filtro log-Gabor
Filtros monogénicos
Congruencia de fase
Edge detection
Fourier transform
Local energy
Log-Gabor filter
Monogenic filters
Phase congruency
dc.subject.proposal.eng.fl_str_mv Edge detection
Fourier transform
Local energy
Log-Gabor filter
Monogenic filters
Phase congruency
description Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-09
dc.date.accessioned.none.fl_str_mv 2025-08-29T13:24:59Z
dc.date.available.none.fl_str_mv 2025-08-29T13:24:59Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv Forero, M., Jacanamejoy, C., Machado, M. y Penagos, K. (2023). Generalized Quantification Function of Monogenic Phase Congruency. Mathematics, 11(17). DOI: 10.3390/math11173795
dc.identifier.doi.none.fl_str_mv 10.3390/math11173795
dc.identifier.issn.none.fl_str_mv 22277390
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/5555
dc.identifier.url.none.fl_str_mv https://www.mdpi.com/2227-7390/11/17/3795
identifier_str_mv Forero, M., Jacanamejoy, C., Machado, M. y Penagos, K. (2023). Generalized Quantification Function of Monogenic Phase Congruency. Mathematics, 11(17). DOI: 10.3390/math11173795
10.3390/math11173795
22277390
url https://hdl.handle.net/20.500.12313/5555
https://www.mdpi.com/2227-7390/11/17/3795
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationissue.none.fl_str_mv 17
dc.relation.citationstartpage.none.fl_str_mv 3795
dc.relation.citationvolume.none.fl_str_mv 11
dc.relation.ispartofjournal.none.fl_str_mv Mathematics
dc.relation.references.none.fl_str_mv Muntarina, K.; Shorif, S.B.; Uddin, M.S. Notes on edge detection approaches. Evol. Syst. 2022, 13, 169–182.
Roberts, L. Machine Perception of 3-D Solids, Optical and Electro-Optical Information Processing; MIT Press: Cambridge, MA, USA, 1965.
Prewitt, J.M. Object enhancement and extraction. Pict. Process. Psychopictorics 1970, 10, 15–19.
Sobel, I. An Isotropic 3 × 3 Image Gradient Operator. In Presentation at Stanford A.I. Project 1968; Academic Press: Cambridge, MA, USA, 2014; pp. 1–5.
Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, PAMI-8, 679–698.
Deriche, R. Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis. 1987, 1, 167–187
Choi, K.H.; Ha, J.E. An Adaptive Threshold for the Canny Edge With Actor-Critic Algorithm. IEEE Access 2023, 11, 67058–67069.
Sultana, S.; Ahmed, B.; Paul, M.; Islam, M.R.; Ahmad, S. Vision-Based Robust Lane Detection and Tracking in Challenging Conditions. IEEE Access 2023, 11, 67938–67955.
Yang, Z.; Zhang, M.; Li, C.; Meng, Z.; Li, Y.; Chen, Y.; Liu, L. Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network. IEEE Access 2022, 10, 77191–77204.
Mubashar, M.; Khan, N.; Sajid, A.R.; Javed, M.H.; Hassan, N.U. Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques. IEEE Access 2022, 10, 70541–70552.
Jia, L.; Dong, J.; Huang, S.; Liu, L.; Zhang, J. Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency. Electronics 2023, 12, 1635.
Koley, S.; Roy, H.; Dhar, S.; Bhattacharjee, D. Illumination invariant face recognition using Fused Cross Lattice Pattern of Phase Congruency (FCLPPC). Inf. Sci. 2022, 584, 633–648.
Fan, J.; Ye, Y.; Li, J.; Liu, G.; Li, Y. A Novel Multiscale Adaptive Binning Phase Congruency Feature for SAR and Optical Image Registration. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–16.
Forero, M.G.; Jacanamejoy, C.A. Unified Mathematical Formulation of Monogenic Phase Congruency. Mathematics 2021, 9, 3080.
Marr, D.; Hildreth, E. Theory of edge detection. Proc. R. Soc. London Ser. Biol. Sci. 1980, 207, 187–217.
Nachar, R.; Inaty, E.; Bonnin, P.J.; Alayli, Y. Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance. Pattern Anal. Appl. 2020, 23, 213–224.
Shrivakshan, G.; Chandrasekar, C. A comparison of various edge detection techniques used in image processing. Int. J. Comput. Sci. Issues (IJCSI) 2012, 9, 269.
Kovesi, P. Image features from phase congruency. Videre J. Comput. Vis. Res. 1999, 1, 1–26.
Kazubek, M. Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Signal Process. Lett. 2003, 10, 324–326.
Kovesi, P. Edges are not just steps. In Proceedings of the Fifth Asian Conference on Computer Vision, Melbourne, Australia, 22–25 January 2002; Volume 8, pp. 22–28.
Kovesi, P. Invariant measures of image features from phase information. Ph.D. Thesis, University of Western Australia, Crawley, Australia, 1996.
Lijuan, W.; Changsheng, Z.; Ziyu, L.; Bin, S.; Haiyong, T. Image feature detection based on phase congruency by Monogenic filters. In Proceedings of the Control and Decision Conference (2014 CCDC), The 26th Chinese, Changsha, China, 31 May–2 June 2014; pp. 2033–2038.
Shi, M.; Zhao, X.; Qiao, D.; Xu, B.; Li, C. Conformal monogenic phase congruency model-based edge detection in color images. Multimed. Tools Appl. 2019, 78, 10701–10716.
Jacanamejoy, C.; Meneses-Casas, N.; Forero, M.G. Image Feature Detection Based on Phase Congruency by Monogenic Filters with New Noise Estimation. In Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Aveiro, Portugal, 4–6 May 2019; pp. 577–588.
Forero, M.G.; Jacanamejoy, C.A.; Rivera-Nieto, S. Study of phase congruency quantization function properties for image edge detection. In Applications of Digital Image Processing XLIV; Tescher, A.G., Ebrahimi, T., Eds.; International Society for Optics and Photonics; SPIE: Bellingham, WA, USA, 2021; Volume 11842, pp. 472–490.
Morrone, M.C.; Ross, J.; Burr, D.C.; Owens, R. Mach bands are phase dependent. Nature 1986, 324, 250–253.
Morrone, M.C.; Owens, R.A. Feature detection from local energy. Pattern Recognit. Lett. 1987, 6, 303–313
Felsberg, M.; Sommer, G. A new extension of linear signal processing for estimating local properties and detecting features. In Mustererkennung 2000; Springer: Berlin/Heidelberg, Germany, 2000; pp. 195–202
Kovesi, P. MATLAB and Octave Functions for Computer Vision and Image Processing. 2013. Available online: https://www.peterkovesi.com/matlabfns/ (accessed on 17th August 2023).
Collins, T.J. ImageJ for microscopy. Biotechniques 2007, 43, S25–S30.
Mapurisa, W.; Sithole, G. Improved Edge Detection for Satellite Images. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 2, 185–192.
Putri, F.N.R.; Wibowo, N.C.H.; Mustofa, H. Clustering of Tuberculosis and Normal Lungs Based on Image Segmentation Results of Chan-Vese and Canny with K-Means. Indones. J. Artif. Intell. Data Min. 2023, 6, 18–28.
Zainuddin, S.N.A.N.; Jumadi, N.A. A Prototype Design and Image Quality Assessment of Low-Cost Finger Vein Image Acquisition using Logitech Webcam. Evol. Electr. Electron. Eng. 2023, 4, 579–588.
Aliu, A.A.; Ariff, N.R.M.; Ametefe, D.S.; John, D. Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation. J. Build. Pathol. Rehabil. 2023, 8, 28.
Dice, L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26, 297–302
Sorensen, T.A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skar. 1948, 5, 1–34.
Abdou, I.; Pratt, W. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc. IEEE 1979, 67, 753–763.
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spelling Forero, Manuel G.221ba9eb-1b06-4908-aac9-b50cd974a391-1Jacanamejoy, Carlos A5e5025dc-501e-4ce6-b554-fbf8def7d042-1Machado, Maximiliano95dbe28a-f6be-434b-9bea-787f7aae5656-1Penagos, Karla L.719243bb-c9e1-41d1-a570-71768e8335ec-12025-08-29T13:24:59Z2025-08-29T13:24:59Z2023-09Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.application/pdfForero, M., Jacanamejoy, C., Machado, M. y Penagos, K. (2023). Generalized Quantification Function of Monogenic Phase Congruency. Mathematics, 11(17). DOI: 10.3390/math1117379510.3390/math1117379522277390https://hdl.handle.net/20.500.12313/5555https://www.mdpi.com/2227-7390/11/17/3795engMultidisciplinary Digital Publishing Institute (MDPI)Suiza17379511MathematicsMuntarina, K.; Shorif, S.B.; Uddin, M.S. Notes on edge detection approaches. Evol. Syst. 2022, 13, 169–182.Roberts, L. Machine Perception of 3-D Solids, Optical and Electro-Optical Information Processing; MIT Press: Cambridge, MA, USA, 1965.Prewitt, J.M. Object enhancement and extraction. Pict. Process. Psychopictorics 1970, 10, 15–19.Sobel, I. An Isotropic 3 × 3 Image Gradient Operator. In Presentation at Stanford A.I. Project 1968; Academic Press: Cambridge, MA, USA, 2014; pp. 1–5.Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, PAMI-8, 679–698.Deriche, R. Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis. 1987, 1, 167–187Choi, K.H.; Ha, J.E. An Adaptive Threshold for the Canny Edge With Actor-Critic Algorithm. IEEE Access 2023, 11, 67058–67069.Sultana, S.; Ahmed, B.; Paul, M.; Islam, M.R.; Ahmad, S. Vision-Based Robust Lane Detection and Tracking in Challenging Conditions. IEEE Access 2023, 11, 67938–67955.Yang, Z.; Zhang, M.; Li, C.; Meng, Z.; Li, Y.; Chen, Y.; Liu, L. Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network. IEEE Access 2022, 10, 77191–77204.Mubashar, M.; Khan, N.; Sajid, A.R.; Javed, M.H.; Hassan, N.U. Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques. IEEE Access 2022, 10, 70541–70552.Jia, L.; Dong, J.; Huang, S.; Liu, L.; Zhang, J. Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency. Electronics 2023, 12, 1635.Koley, S.; Roy, H.; Dhar, S.; Bhattacharjee, D. Illumination invariant face recognition using Fused Cross Lattice Pattern of Phase Congruency (FCLPPC). Inf. Sci. 2022, 584, 633–648.Fan, J.; Ye, Y.; Li, J.; Liu, G.; Li, Y. A Novel Multiscale Adaptive Binning Phase Congruency Feature for SAR and Optical Image Registration. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–16.Forero, M.G.; Jacanamejoy, C.A. Unified Mathematical Formulation of Monogenic Phase Congruency. Mathematics 2021, 9, 3080.Marr, D.; Hildreth, E. Theory of edge detection. Proc. R. Soc. London Ser. Biol. Sci. 1980, 207, 187–217.Nachar, R.; Inaty, E.; Bonnin, P.J.; Alayli, Y. Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance. Pattern Anal. Appl. 2020, 23, 213–224.Shrivakshan, G.; Chandrasekar, C. A comparison of various edge detection techniques used in image processing. Int. J. Comput. Sci. Issues (IJCSI) 2012, 9, 269.Kovesi, P. Image features from phase congruency. Videre J. Comput. Vis. Res. 1999, 1, 1–26.Kazubek, M. Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Signal Process. Lett. 2003, 10, 324–326.Kovesi, P. Edges are not just steps. In Proceedings of the Fifth Asian Conference on Computer Vision, Melbourne, Australia, 22–25 January 2002; Volume 8, pp. 22–28.Kovesi, P. Invariant measures of image features from phase information. Ph.D. Thesis, University of Western Australia, Crawley, Australia, 1996.Lijuan, W.; Changsheng, Z.; Ziyu, L.; Bin, S.; Haiyong, T. Image feature detection based on phase congruency by Monogenic filters. In Proceedings of the Control and Decision Conference (2014 CCDC), The 26th Chinese, Changsha, China, 31 May–2 June 2014; pp. 2033–2038.Shi, M.; Zhao, X.; Qiao, D.; Xu, B.; Li, C. Conformal monogenic phase congruency model-based edge detection in color images. Multimed. Tools Appl. 2019, 78, 10701–10716.Jacanamejoy, C.; Meneses-Casas, N.; Forero, M.G. Image Feature Detection Based on Phase Congruency by Monogenic Filters with New Noise Estimation. In Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Aveiro, Portugal, 4–6 May 2019; pp. 577–588.Forero, M.G.; Jacanamejoy, C.A.; Rivera-Nieto, S. Study of phase congruency quantization function properties for image edge detection. In Applications of Digital Image Processing XLIV; Tescher, A.G., Ebrahimi, T., Eds.; International Society for Optics and Photonics; SPIE: Bellingham, WA, USA, 2021; Volume 11842, pp. 472–490.Morrone, M.C.; Ross, J.; Burr, D.C.; Owens, R. Mach bands are phase dependent. Nature 1986, 324, 250–253.Morrone, M.C.; Owens, R.A. Feature detection from local energy. Pattern Recognit. Lett. 1987, 6, 303–313Felsberg, M.; Sommer, G. A new extension of linear signal processing for estimating local properties and detecting features. In Mustererkennung 2000; Springer: Berlin/Heidelberg, Germany, 2000; pp. 195–202Kovesi, P. MATLAB and Octave Functions for Computer Vision and Image Processing. 2013. Available online: https://www.peterkovesi.com/matlabfns/ (accessed on 17th August 2023).Collins, T.J. ImageJ for microscopy. Biotechniques 2007, 43, S25–S30.Mapurisa, W.; Sithole, G. Improved Edge Detection for Satellite Images. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 2, 185–192.Putri, F.N.R.; Wibowo, N.C.H.; Mustofa, H. Clustering of Tuberculosis and Normal Lungs Based on Image Segmentation Results of Chan-Vese and Canny with K-Means. Indones. J. Artif. Intell. Data Min. 2023, 6, 18–28.Zainuddin, S.N.A.N.; Jumadi, N.A. A Prototype Design and Image Quality Assessment of Low-Cost Finger Vein Image Acquisition using Logitech Webcam. Evol. Electr. Electron. Eng. 2023, 4, 579–588.Aliu, A.A.; Ariff, N.R.M.; Ametefe, D.S.; John, D. Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation. J. Build. Pathol. Rehabil. 2023, 8, 28.Dice, L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26, 297–302Sorensen, T.A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skar. 1948, 5, 1–34.Abdou, I.; Pratt, W. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc. IEEE 1979, 67, 753–763.© 2023 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/https://www.mdpi.com/2227-7390/11/17/3795Fase MonogénicaDetección de bordesTransformada de FourierEnergía localFiltro log-GaborFiltros monogénicosCongruencia de faseEdge detectionFourier transformLocal energyLog-Gabor filterMonogenic filtersPhase congruencyGeneralized Quantification Function of Monogenic Phase CongruencyArtículo 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/plain2532https://repositorio.unibague.edu.co/bitstreams/2831d88e-6eff-4d65-9a15-7f666b6b604b/download958a895ffd9ff1c60c77c2d37315be99MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg21450https://repositorio.unibague.edu.co/bitstreams/8c2c90dc-bb9d-46be-9b57-d03daa91fdb5/download3015712561242e1698fb56ed10663b50MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/73ff39c9-0857-41ad-a198-fe8fd5fcaefb/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf86721https://repositorio.unibague.edu.co/bitstreams/1d65cad7-e144-45f1-8aa7-7ccd8afdb58d/download20e49b049333e51eae3261d036531892MD5220.500.12313/5555oai:repositorio.unibague.edu.co:20.500.12313/55552025-09-12 12:06:58.292https://creativecommons.org/licenses/by-nc/4.0/© 2023 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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