Amo, Ana del and Sobrevilla, P. and Montseny, E. and Montero, Javier (2004) Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm. In NAFIPS 2004: Ammual meeting of the north american fuzzy information processing society,vols 1and 2: fuzzy sets in the heart of the canadianI rockies. IEEE Conference Publications, 1 . IEEE, Banff, Canada, pp. 486-490. ISBN 0-7803-8376-1
Restringido a Repository staff only
The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology  can be improved by applying a fuzzy classification algorithm  based on a fuzzy outranking methodology  as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes.
|Item Type:||Book Section|
Annual Meeting of the North-American-Fuzzy-Information-Processing-Society
|Uncontrolled Keywords:||Computer Science; Artificial Intelligence; Computer Science; Information Systems|
|Subjects:||Sciences > Computer science > Artificial intelligence|
J.C. Bezdek, and S. K. Pal Fuzzy Models for Pattern Recognition. New YorkIEEE Press 1992.
A. Del Amo, J. Montero and G. Biging, “Classifying pixels by means of fuzzy relations,”Inter. J.General Systems, Vol. 29, pp. 605-621, 1999.
A. Del Amo, J. Montero, A. Fernandez, M. Lopez,J.Tordesillas and G. Biging, “Spectral fuzzy classification:an application,” IEEE Tmns on Systems,Man and Cybernetics, Part C, Vol. 32,pp.42-48, Feb. 2002.
A. Del Amo, J. Montero, and E. Molina, “Representation of consistent recursive rules”, European Journal of Operational Research, Vol. 130, pp. 53, 2001.
A. Del Amo, D. Gomez, J. Montero and G. Biging:”Relevance and redundancy in fuzzy classification systems”. Mathware and Soft Computing 8203-216, 2001.
A. Del Amo, J. Montero, G. Biging and V. Cutello:”Fuzzy classification systems”. European Journal of Operational Research, to appear.
E.Montseny,P.Sobrevilla. ”Application of fuzzy techniques to the design of algorithms in computer vision”, Mathware d Soft Computing, Vol. 2-3,1998, pp.223-230. J. R. Jensen Introductory Digital Image Processing.A Remote Sensing Perspectiwe. Prentice Hall
P.P. Perny and B. Roy, “The use of fuzzy outranking relations in preference modelling”, Fuzzy Sets and Systems Vol. 49, pp. 33-53, 1992.
Pratt W., Digital image processing, John Wiley and Sons, 1978.
A.R. Robertson, ”Color perception”, Physics Today,1992, pp. 2429.
S. Romani, E. Montseny, P. Sobrevilla. ”Obtaining the Relevant Colors of an image through Stabilitybased Fuzzy Color Histograms”, Proc. of the 12th IEEE International Conference on Fuzzy Systems,Sant Louis (MO), 2003, pp 914919.
A.R. Smith, ”Color gamut transform pairs”, IEEE Tmns on Computer Graphics, Vol. 2, 1978, pp. 12-19.
J. Siskos, J. Lochard and J. Lombard , “A multicriteria decision making methodology under fuzziness:application to the evaluation of radiological protection in nuclear power plants, Fuzzy Sets and Decision Analysis in H.J.Zimmermann, L.A.Zadeh and B.R. Gaines (eds.) North Holland, Amsterdam,1984.
D. Yagi, K. Abe, H. Nakatani, ”Segmentation of color aerial photographs using HSV color models”,IAPR Workshop on Machine Vision Applications.(Tokyo), 1992.490
|Deposited On:||31 Oct 2012 09:29|
|Last Modified:||19 Apr 2016 16:29|
Repository Staff Only: item control page