A new approach to Color Edge Detection

Simple item page
dc.book.titleProceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
dc.conference.date9-13 Sep 2019
dc.conference.placePraga, República Checa
dc.conference.title11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
dc.contributor.authorFlores Vidal, Pablo Arcadio
dc.contributor.authorGómez González, Daniel
dc.contributor.authorCastro Cantalejo, Javier
dc.contributor.authorVillarino, Guillermo
dc.contributor.authorMontero, Javier
dc.date.accessioned2023-06-17T14:22:39Z
dc.date.available2023-06-17T14:22:39Z
dc.date.issued2019-08
dc.description.abstractMost edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem. In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms. In this research, it is been used the set of images from Berkeley. In order to evaluate the algorithm’s performance, F measure is computed. The way that color information -the different channels- is aggregated is proved to be relevant for the edge detection task. Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels).
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía y Competitividad (MINECO)
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/63642
dc.identifier.citation[1] S. Bogumil, Color image edge detection and segmentation: A comparison of the vector angle and the euclidean distance color similarity measures, Ph.D. thesis, University of Waterloo (1999). [2] J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis y Machine Intelligence PAMI-8 (6) (1986) 679–698. [3] B. de Baets, C. López-Molina, The kermit image toolkit (kitt), Ghent university, www.kermitimagetoolkit.net. [4] J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine learning research 7 (Jan) (2006) 1–30. [5] S. Di Zenzo, A note on the gradient of a multiimage, Computer vision, graphics, and image processing 33 (1) (1986) 116–125. [6] S. Dutta, A color edge detection algorithm in rgb color space, 2009, pp. 337–340. [7] P. Flores-Vidal, N. Martínez, D. Gómez, Postprocessing in edge detection based on segments, in: Proceedings of the 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support, World Scientific Proceedings Series on Computer Engineering and Information Science (Belfast), World Scientific, 2018, p. 11. [8] P. A. Flores-Vidal, J. Montero, D. Gómez, G. Villarino, A new edge detection method based on global evaluation using supervised classification algorithms, International Journal of Computational Intelligence Systems 11 (1). [9] P. A. Flores-Vidal, P. Olaso, D. Gómez, C. Guada, A new edge detection method based on global evaluation using fuzzy clustering, Soft Computing (2018) 1–13. [10] S. García, A. Fernández, J. Luengo, F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences 180 (10) (2010) 2044–2064. [11] C. Guada, D. Gómez, J. T. Rodríguez, J. Yáñez, J. Montero, Classifying image analysis techniques from their output, International Journal of Computational Intelligence Systems 9 (2016) 43–68, cited By :4. [12] C. Guada, D. Gómez, J. T. Rodríguez, J. Yáñez, J. Montero, Fuzzy image segmentation based on the hierarchical divide y link clustering algorithm, in: Proceedings - The 2015 10th International Conference on Intelligent Systems y Knowledge Engineering, ISKE 2015, 2016, pp. 12–17. [13] M. Heath, S. Sarkar, T. Sanocki, K. Bowyer, Comparison of edge detectors: A methodology and initial study, Computer Vision and Image Understanding 69 (1) (1998) 38–54. [14] S. Holm, A simple sequentially rejective multiple test procedure, Scandinavian journal of statistics (1979) 65–70. [15] N. P. Jacobson, M. R. Gupta, Design goals and solutions for display of hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing 43 (11) (2005) 2684–2692. [16] D. Marr, Vision: a Computational Investigation into the Human Representation y Processing of Visual Information, 1982. [17] D. R. Martin, C. Fowlkes, D. Tal, J. Malik, A database of human segmented natural images y its application to evaluating segmentation algorithms y measuring ecological statistics, in: Proceedings of the IEEE International Conference on Computer Vision, Vol. 2, 2001, pp. 416–423. [18] M. A. Ruzon, C. Tomasi, Color edge detection with the compass operator, in: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Vol. 2, 1999, pp. 160–166 Vol. 2. [19] I. Sobel, History and definition of the so-called ”sobel operator”, more appropriately named the sobel-feldman operator. [20] P. E. Trahanias, A. N. Venetsanopoulos, Color edge detection using vector order statistics, IEEE Transactions on Image Processing 2 (2) (1993) 259–264. [21] F. Wilcoxon, Individual comparisons by ranking methods, Biometrics bulletin 1 (6) (1945) 80–83. [22] Y. Yang, Colour edge detection and segmentation using vector analysis, 1995.
dc.identifier.doi10.2991/eusflat-19.2019.53
dc.identifier.officialurlhttps://doi.org/10.2991/eusflat-19.2019.53
dc.identifier.relatedurlhttps://www.atlantis-press.com/proceedings/eusflat-19/125914823
dc.identifier.urihttps://hdl.handle.net/20.500.14352/14190
dc.language.isoeng
dc.page.final384
dc.page.initial376
dc.relation.projectIDTIN2015-66471-P
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.keywordColor edge detection
dc.subject.keywordMultichannel edge detection
dc.subject.keywordRGB
dc.subject.keywordPreaggregation
dc.subject.keywordPost-aggregation
dc.subject.keywordCrispy aggregation
dc.subject.keywordFuzzy aggregation.
dc.subject.ucmInformática (Informática)
dc.subject.ucmMatemáticas (Matemáticas)
dc.subject.unesco1203.17 Informática
dc.subject.unesco12 Matemáticas
dc.titleA new approach to Color Edge Detection
dc.typeconference paper
dspace.entity.typePublication
relation.isAuthorOfPublication881ba82f-e783-4e7e-a1b6-dded36681497
relation.isAuthorOfPublication4dcf8c54-8545-4232-8acf-c163330fd0fe
relation.isAuthorOfPublicatione556dae6-6552-4157-b98a-904f3f7c9101
relation.isAuthorOfPublication.latestForDiscovery881ba82f-e783-4e7e-a1b6-dded36681497
Download
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
125914823.pdf
Size:
5.09 MB
Format:
Adobe Portable Document Format
downoload Download
Collections
Ponencias o Pósters de Seminarios, Congresos, etc