RT Conference Proceedings
T1 New aggregation strategies in color edge detection with HSV Images
A1 Flores Vidal, Pablo Arcadio
A1 Gómez González, Daniel
A1 Castro Cantalejo, Javier
A1 Montero De Juan, Francisco Javier
A2 Ciucci, Davide
AB Most 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.
SN 978-3-031-08971-8
YR 2022
FD 2022-07
LK https://hdl.handle.net/20.500.14352/110389
UL https://hdl.handle.net/20.500.14352/110389
LA eng
NO Flores-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.
DS Docta Complutense
RD 10 abr 2025