RT Journal Article
T1 Postprocessing of edge detection algorithms with machine learning techniques
A1 Flores Vidal, Pablo Arcadio
A1 Castro Cantalejo, Javier
A1 Gómez González, Daniel
A2 Hemanth, Jude
AB In 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.
PB Hindawi Limited
SN 1024-123X
YR 2022
FD 2022-03-23
LK https://hdl.handle.net/20.500.14352/110385
UL https://hdl.handle.net/20.500.14352/110385
LA eng
NO Flores-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.
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 5 abr 2025