CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

id sigradi2010_324
authors Toranzo, Lorca Gustavo; Pereira Barzaga Osvaldo; Arzola Ruiz José
year 2010
title Segmentación de imágenes digitales mediante técnicas de clustering complementadas con técnicas de crecimiento de regiones [Digital image segmentation through clustering techniques, enhanced by region growing techniques]
source SIGraDi 2010_Proceedings of the 14th Congress of the Iberoamerican Society of Digital Graphics, pp. Bogotá, Colombia, November 17-19, 2010, pp. 324-327
summary This paper proposes a solution to the problem of digital image segmentation based on clustering techniques. K - means and fuzzy k - means are clustering algorithms detailed within this study. We propose novel modifications to k - means and fuzzy k - means algorithms to vary objective functions and to use color difference as a distance measure. These new algorithms are guaranteed to obtain the same results as the originals while considerably reducing the execution time. Pixel growing is used to obtain connected structures in the results of clustering algorithms.
keywords clustering; fuzzy k - means; image segmentation; k - means
series SIGRADI
email gtoranzo@uci.cu
full text file.pdf (159,130 bytes)
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100%; open González, R. C. y Wood, R. E. (2001) Find in CUMINCAD Digital image processing , New Jersey: Prentice Hall
100%; open Jain, A. K., Murty, M. N. y Flynn, P. J. (1999) Find in CUMINCAD Data clustering: a Review , ACM Computing Surveys, 265-317
100%; open Sonka, M., Hlavac, V. y Boyle, R. (1993) Find in CUMINCAD Image processing: analysis and machine vision , Capmam and Hall Computing

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