Innovative applications of associative morphological memories for image processing and pattern recognition
Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyper spectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2003 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/1774 |
| Acceso en línea: | https://hdl.handle.net/2099/1774 |
| Access Level: | acceso abierto |
| Palabra clave: | Image retrieval Morphological associative memories Hyper spectral image unsupervised segmentation Imatges -- Processament -- Tècniques digitals -- Models matemàtics Reconeixement de formes (Informàtica) Classificació AMS::68 Computer science::68U Computing methodologies and applications |
| Sumario: | Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyper spectral 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). |
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