Postprocessing of edge detection algorithms with machine learning techniques
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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/110385 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/110385 |
| Access Level: | acceso abierto |
| Palabra clave: | 510.5 519.712 007 004.8 004.6 Classification (of information) Edge detection Image segmentation Learning algorithms Machine learning Signal detection Estadística Inteligencia artificial (Informática) Gestión de la información Técnicas de la imagen 1206.01 Construcción de Algoritmos 1209 Estadística 1203.04 Inteligencia Artificial 1209.03 Análisis de Datos 2209.90 Tratamiento Digital. Imágenes |
| Sumario: | 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. |
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