Flores Vidal, Pablo ArcadioCastro Cantalejo, JavierGómez González, DanielHemanth, Jude2024-11-112024-11-112022-03-23Flores-Vidal, P., Castro, J. y Gómez, D. (2022) «Postprocessing of Edge Detection Algorithms with Machine Learning Techniques», Mathematical Problems in Engineering, 2022. Disponible en: https://doi.org/10.1155/2022/9729343.1024-123Xhttps://hdl.handle.net/20.500.14352/110385In this paper, machine learning (ML) techniques are applied at an early stage of Image Processing (IP). The learning procedures are usually applied from at least the image segmentation level, whereas, in this paper, this is done from a lower processing level: the edge detection level (ED). The main objective is to solve the edge detection problem through ML techniques. The proposed methodology is based on a classification of edges made pixel by pixel, but the predictors employed for the ML task include information about the pixel neighborhood and structures of connected pixels called edge segments. The Sobel operator is employed as input. Making use of 50 images that belong to the Berkeley Computer Vision data set, the average performance of the validation sets when employing our Neural Networks method reached an F-measure significatively higher than with the Sobel operator. The experiment results show that our post-processing technique is a promising new approach for ED.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article1563-5147https://doi.org/10.1155/2022/9729343https://onlinelibrary.wiley.com/journal/2629open access510.5519.712007004.8004.6Classification (of information)Edge detectionImage segmentationLearning algorithmsMachine learningSignal detectionEstadísticaInteligencia artificial (Informática)Gestión de la informaciónTécnicas de la imagen1206.01 Construcción de Algoritmos1209 Estadística1203.04 Inteligencia Artificial1209.03 Análisis de Datos2209.90 Tratamiento Digital. Imágenes