Image Processing Using Pulse-Coupled Neural Networks / by T. Lindblad, J.M. Kinser.

Por:

Lindblad, T [autor]

Colaborador(es):

Tipo de material: Texto

TextoEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Edición: Second, Revised EditionDescripción: xI, 164 páginas 140 ilustraciones recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9783540282938

Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:

TK5102.9

Recursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificaciÃ³n en Biblioteca Digital UANL)

Contenidos:

and Theory -- Theory of Digital Simulation -- Automated Image Object Recognition -- Image Fusion -- Image Texture Processing -- Image Signatures -- Miscellaneous Applications -- Hardware Implementations.

Resumen: This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture, segments, and edges from images. As these attributes form the foundations of most image processing tasks, the use of PCNNs facilitates traditional tasks such as recognition, foveation, and image fusion. PCNN technology has also paved the way for new image processing techniques such as object isolation, spiral image fusion, image signatures, and content-based image searches. This volume contains examples of several image processing applications, as well as a review of hardware implementations.

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Springer eBooks

This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture, segments, and edges from images. As these attributes form the foundations of most image processing tasks, the use of PCNNs facilitates traditional tasks such as recognition, foveation, and image fusion. PCNN technology has also paved the way for new image processing techniques such as object isolation, spiral image fusion, image signatures, and content-based image searches. This volume contains examples of several image processing applications, as well as a review of hardware implementations.

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