A linear time implementation of k-means for multilevel thresholding of grayscale images

Pablo Fonseca, Jacques Wainer

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Resumen

In this paper we present a method based on the k-means algorithm for multilevel thresholding of grayscale images. The clustering is computed over the histogram rather than on the full list of intensity levels. Our implementation runs in linear time per iteration proportional to the number of bins of the histogram, not depending on the size of the image nor on the number of clusters/levels as in a traditional implementation. Therefore, it is possible to get a large speedup when the number of bins of the histogram is significantly shorter than the number of pixels. In order to achieve that running time, two restrictions were exploited in our implementation: (I) we target only grayscale images and (II) thresholding does not use spatial information.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
EditoresEduardo Bayro-Corrochano, Edwin Hancock
EditorialSpringer Verlag
Páginas120-126
Número de páginas7
ISBN (versión digital)9783319125671
DOI
EstadoPublicada - 2014
Publicado de forma externa
Evento19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, México
Duración: 2 nov. 20145 nov. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8827
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
País/TerritorioMéxico
CiudadPuerto Vallarta
Período2/11/145/11/14

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