Resumen
Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the probability density function. In our algorithm, pixels in the image with minimum value of density function are labeled as edges. The boundary between two homogeneous regions is defined in two domains: the spatial/lattice domain and the range/color domain. Extensive experimental evaluations proved that our edge detection method is significantly a competitive algorithm.
Idioma original | Inglés |
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Título de la publicación alojada | Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 |
Editores | Hamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti |
Editorial | CSREA Press |
Páginas | 123-128 |
Número de páginas | 6 |
ISBN (versión digital) | 1601324642, 9781601324641 |
Estado | Publicada - 2017 |
Evento | 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 - Las Vegas, Estados Unidos Duración: 17 jul. 2017 → 20 jul. 2017 |
Serie de la publicación
Nombre | Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 |
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Conferencia
Conferencia | 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 |
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País/Territorio | Estados Unidos |
Ciudad | Las Vegas |
Período | 17/07/17 → 20/07/17 |
Nota bibliográfica
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