Innovative applications of associative morphological memories for image processing and pattern recognition.

Graña, Manuel, Sussner, Peter, and Ritter, Gerhard. "Innovative applications of associative morphological memories for image processing and pattern recognition.." Mathware and Soft Computing 10.2-3 (2003): 155-168. <http://eudml.org/doc/39257>.

@article{Graña2003,
abstract = {Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyperspectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of morphological independent patterns define a feature extraction process that is the basis for the image processing applications. We discuss some experimental results on the fish shape data base and on a synthetic hyperspectral image, including the comparison with other linear feature extraction algorithms (ICA and CCA).},
author = {Graña, Manuel, Sussner, Peter, Ritter, Gerhard},
journal = {Mathware and Soft Computing},
keywords = {Lógica difusa; Redes neuronales; Procesamiento de imágenes; Reconocimiento de patrones},
language = {eng},
number = {2-3},
pages = {155-168},
title = {Innovative applications of associative morphological memories for image processing and pattern recognition.},
url = {http://eudml.org/doc/39257},
volume = {10},
year = {2003},
}

TY - JOUR
AU - Graña, Manuel
AU - Sussner, Peter
AU - Ritter, Gerhard
TI - Innovative applications of associative morphological memories for image processing and pattern recognition.
JO - Mathware and Soft Computing
PY - 2003
VL - 10
IS - 2-3
SP - 155
EP - 168
AB - Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyperspectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of morphological independent patterns define a feature extraction process that is the basis for the image processing applications. We discuss some experimental results on the fish shape data base and on a synthetic hyperspectral image, including the comparison with other linear feature extraction algorithms (ICA and CCA).
LA - eng
KW - Lógica difusa; Redes neuronales; Procesamiento de imágenes; Reconocimiento de patrones
UR - http://eudml.org/doc/39257
ER -