Edge detection based on kernel density estimation

O. Pereira, Y. Garcés, R. Rodríguez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

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 originalInglés estadounidense
Título de la publicación alojadaProceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017
EditoresHamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti
EditorialCSREA Press
Páginas123-128
Número de páginas6
ISBN (versión digital)1601324642, 9781601324641
EstadoPublicada - 1 ene 2017
Evento2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 - Las Vegas, Estados Unidos
Duración: 17 jul 201720 jul 2017

Serie de la publicación

NombreProceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017

Conferencia

Conferencia2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017
País/TerritorioEstados Unidos
CiudadLas Vegas
Período17/07/1720/07/17

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