Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Line segment detection using weighted Mean Shift procedures on a 2D Slice sampling strategy
Año:2011
Áreas de investigación
  • Procesado y análisis de la señal
Datos
Descripción
A new line segment detection approach is introduced in this paper for its application in real-time computer vision systems. It has been designed to work unsupervised without any prior knowledge of the imaged scene; hence, it does not require tuning of input parameters. Although many works have been presented on this topic, as far as we know, none of them achieves a trade-off between accuracy and speed as our strategy does. The reduction of the computational cost compared to other fast methods is based on a very efficient sampling strategy that sequentially proposes points on the image that likely belong to line segments. Then, a fast line growing algorithm is applied based on the Bresenham algorithm, which is combined with a modified version of the mean shift algorithm to provide accurate line segments while being robust against noise. The performance of this strategy is tested for a wide variety of images, comparing its results with popular state-of-the-art line segment detection methods. The results show that our proposal outperforms these works considering simultaneously accuracy in the results and processing speed.
Internacional
Si
JCR del ISI
Si
Título de la revista
PATTERN ANALYSIS AND APPLICATIONS
ISSN
1433-7541
Factor de impacto JCR
1,293
Información de impacto
Volumen
14
DOI
10.1007/s10044-011-0211-4
Número de revista
2
Desde la página
149
Hasta la página
163
Mes
MAYO
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Marcos Nieto Doncel (UPM)
  • Autor: Carlos Cuevas Rodriguez (UPM)
  • Autor: Luis Salgado Alvarez de Sotomayor (UPM)
  • Autor: Narciso Garcia Santos (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Señales, Sistemas y Radiocomunicaciones
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
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