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Segmentación Borrosa de Imágenes basada en un Algoritmo de Segmentación Jerárquica

dc.book.titleActas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial
dc.contributor.authorGuada, Carey
dc.contributor.authorGómez González, Daniel
dc.contributor.authorRodríguez González, Juan Tinguaro
dc.contributor.authorYáñez Gestoso, Francisco Javier
dc.contributor.authorMontero De Juan, Francisco Javier
dc.date.accessioned2023-06-19T15:55:06Z
dc.date.available2023-06-19T15:55:06Z
dc.date.issued2015
dc.descriptionV Simposio de Lógica Difusa y Soft Computing.
dc.description.abstractEl propósito de este trabajo es presentar una forma de como obtener una segmentación borrosa de imágenes a través de un algoritmo de segmentación jerárquica de imágenes. En primer lugar, para alcanzar este objetivo, se definen dos maneras de segmentar una imagen representada por una red, a través de nodos y mediante aristas. Posteriormente,se extiende la segmentación basada en aristas en un contexto borroso y así proponer una definición y visualización de la segmentación borrosa de imágenes. Luego, se presenta un algoritmo de segmentación jerárquica. Finalmente, se muestran los resultados experimentales obtenidos.es
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipComunidad de Madrid
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades (España)
dc.description.sponsorshipLODISCO de Logica Difusa y Soft Computing
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/34889
dc.identifier.isbn978-84-608-4099-2
dc.identifier.officialurlhttp://simd.albacete.org/actascaepia15/papers/00519.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.14352/35795
dc.language.isoeng
dc.page.final528
dc.page.initial519
dc.page.total1265
dc.publication.placeAlbacete
dc.publisherCAEPIA'15
dc.relation.projectIDCASI-CAM (S2013/ICE-2845)
dc.relation.projectIDTIN2012-32482
dc.relation.projectIDTIN2014-56381-REDT
dc.rights.accessRightsrestricted access
dc.subject.cdu004.8
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203.04 Inteligencia Artificial
dc.titleSegmentación Borrosa de Imágenes basada en un Algoritmo de Segmentación Jerárquicaes
dc.typebook part
dcterms.references1. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence. 33, 5:898–916, (2011) 2. Basavaprasad, B., Ravindra, H.: A Survey on raditional and Graph Theoretical Techniques for Image Segmentation. IJCA Proc. on Nat. Conf. on Recent Advances in information Technology. NCRAIT, 1:38–46, (2014) 3. Bustince, H., Mohedano, V., Barrenechea, E., Pagola, M.: Definition and construction of fuzzy DI-subsethood measures. Information Sciences. 176, 21:3190–3231, (2006) 4. Bloch, I.: Fuzzy connectivity and mathematical morphology. Pattern Recognition.14, 483–488, (1993) 5. Bustince, H., Barrenechea, E., Pagola, M., Fern´andez, J.: Interval-valued fuzzy sets constructed from matrices: Application to edge detection. Fuzzy Sets and Systems. 160, 13:1819–1840, (2009) 6. Casillas, J., Cord´on, O., Triguero, F. H., Magdalena, L.: Interpretability issues in fuzzy modeling. Springer. 128, (2013) 7. Chamorro-Mart´ınez, J., S´anchez, D., Prados-Su´arez, B., Gal´an-Perales, E., Vila, M. A.: A hierarchical approach to fuzzy segmentation of colour images. In Fuzzy Systems, 2003. FUZZ’03. The 12th IEEE International Conference. 2, 966–971, (2003) 8. Cheng, H., Sun, Y.: A hierarchical approach to color image segmentation using homogeneity. IEEE Trans. on Image Processing. 9, 12:2071–2082, (2000) 9. Gomez, D., Zarrazola, E., Yañez, J., Rodrıguez, J., montero, J.: A new concept of fuzzy image segmentation. In Decision Making and Soft Computing Proceedings of the 11th International FLINS Conference. World Scientific Proceedings Series on Computer Engineering and Information Science. World Scientific Publishing Company, Singapore. 4, 9:412–417, (2014) 10. Gomez, D., Zarrazola, E., Yañez, J., Montero, J.: A Divide-and-Link Algorithm for Hierarchical Clustering in Networks. Information Sciences. 316, 308–328, (2015) 11. Gomez D., Yañez, J., Guada, C., Rodrıguez, J., montero, J., Zarrazola, E.: Fuzzy Image Segmentation based upon Hierarchical Clustering. Knowledge-Based systems.DOI: 10.1016/j.knosys.2015.07.017, (2015) 12. Lhermitte, S., Verbesselt, J., Jonckheere, I., Nackaerts, K., Van Aardt, J., Verstraeten,W., Coppin, P.: Hierarchical image segmentation based on similarity of NDVI time series. Remote Sensing of Environment. 112, 2:506–521, (2008) 13. Pal, N., Pal, S.: A review on image segmentation techniques. Pattern Recognition.26, 1277–1294, (1993) 14. Pratt, W.: Digital Image Processing. Wiley -Interscience. (2001) 15. Rosenfeld, A.: Fuzzy digital topology. Information Control. 40, 76–87 (1979) 16. Saha, P., Udupa, J., Odhner, D.: Scale-Based Fuzzy Connected Image Segmentation:Theory, Algorithms, and Validation. Computer Vision and Image Understanding. 77, 145–174, (2000) 17. Schroeter, P., Bigun, J.: Hierarchical image segmentation by multi-dimensional clustering and orientation-adaptive boundary refinement. Pattern Recognition. 28, 5:695–709, (1995) 18. Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation a survey of soft computing approaches. International Journal of Recent Trends in Engineering. 1, 2:250–254, (2009) 19. Udupa, J., Samarasekera, S.: Fuzzy Connectedness and Object Definition: Theory,Algorithms, and Applications in Image Segmentation. Graphical Models and Image Processing. 58, 246–261, (1996)
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