Morphological Clustering of the SOM for Multi-dimensional Image Segmentation
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New imaging sensors and technologies challenge the application of traditional computer vision methodologies due to an increment of the image dimensionality. Images processed in different application fields are not any more restricted to the grayvalue image domain, but take into consideration images of larger dimensionality. Color, multisensorial and satellite images are some examples being used in different application fields. This increment in the dimensionality of the problems related to the utilization of these larger feature spaces has been already characterized as a problem denoted by the “curse of dimensionality” in pattern recognition. Taking into consideration feature spaces of large dimensions introduce some geometric anomalies, which hinder the interpretability of the results and thus the attainment of the expected ones . The paper presents a hybrid framework, which makes use of a Self-Organizing Map (SOM)  and the fuzzy integral  in order to cope with the segmentation of images in high-dimensional feature spaces.
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- Morphological Clustering of the SOM for Multi-dimensional Image Segmentation
- Book Title
- Computational Methods in Neural Modeling
- Book Subtitle
- 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part I
- pp 582-589
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- Online ISBN
- Series Title
- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 4. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia
- Author Affiliations
- 5. Dept. Security and Inspection Tech., Fraunhofer IPK, Pascalstr 8-9, 10587, Berlin, Germany
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