Flores Vidal, Pablo ArcadioGómez González, DanielCastro Cantalejo, JavierMontero De Juan, Francisco JavierCiucci, Davide2024-11-112024-11-112022-07Flores-Vidal, P.A., Gómez, D., Castro, J., Montero, J. (2022). New Aggregation Strategies in Color Edge Detection with HSV Images. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham.978-3-031-08971-810.1007/978-3-031-08974-9_29https://hdl.handle.net/20.500.14352/110389Most edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregating color information from RGB and HSV images for edge extraction purposes through the usage of the Canny algorithm. The Berkeley’s image data set is used to evaluate the performance of the different aggregation methods. Precision, Recall and F-score are computed. Better performance of aggregations with HSV channels than with RGB’s was found. This article also shows that depending on the type of image used -RGB or HSV-, some methodologies are more appropriate than others.engconference paperhttps://doi-org.bucm.idm.oclc.org/10.1007/978-3-031-08974-9_29https://link-springer-com.bucm.idm.oclc.org/chapter/10.1007/978-3-031-08974-9_29restricted access519.2004.6519.712Color edge detectionHSVHexcone modelRGBPre-aggregationPost-aggregationCannyEstadísticaColor2209.90 Tratamiento Digital. Imágenes1206.01 Construcción de Algoritmos1209.03 Análisis de Datos