Artículos relacionados a Biologically Inspired Hexagonal Deep Learning for Hexagonal...

Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing - Tapa blanda

 
9783961002139: Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing

Comprar nuevo

Ver este artículo

EUR 11,00 gastos de envío desde Alemania a España

Destinos, gastos y plazos de envío

Resultados de la búsqueda para Biologically Inspired Hexagonal Deep Learning for Hexagonal...

Imagen del vendedor

Tobias Schlosser
ISBN 10: 3961002134 ISBN 13: 9783961002139
Nuevo Taschenbuch
Impresión bajo demanda

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -While current approaches to digital image processing in the context of deep learning are motivated by biological processes in the human brain, they are, however, also limited due to the current state of the art of input and output devices. To generate images from real-world scenes, the underlying lattice formats are predominantly based on rectangular or square structures. Yet, the human visual perception system suggests an alternative approach that manifests itself in the sensory cells of the human eye in the form of hexagonal arrangements.This contribution is therefore concerned with the design, implementation, and evaluation of hexagonal solutions in the form of hexagonal deep neural networks (H-DNN). The realized hexagonal functionality had to be built from the ground up as hexagonal counterparts to otherwise conventional square image processing systems, for which hexagonal equivalents for artificial neural network operations, layers, and models had to be implemented.To enable their evaluation, a set of different application areas within astronomical, medical, and industrial image processing are provided that allow an assessment of H-DNNs in terms of their general performance. The presented results demonstrate the possible benefits of H-DNNs for image processing systems. It is shown that H-DNNs can result in increased classification capabilities given different basic geometric shapes and contours, which in turn partially translate into their real-world applications. 272 pp. Englisch. Nº de ref. del artículo: 9783961002139

Contactar al vendedor

Comprar nuevo

EUR 32,90
Convertir moneda
Gastos de envío: EUR 11,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Tobias Schlosser
ISBN 10: 3961002134 ISBN 13: 9783961002139
Nuevo Taschenbuch
Impresión bajo demanda

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - While current approaches to digital image processing in the context of deep learning are motivated by biological processes in the human brain, they are, however, also limited due to the current state of the art of input and output devices. To generate images from real-world scenes, the underlying lattice formats are predominantly based on rectangular or square structures. Yet, the human visual perception system suggests an alternative approach that manifests itself in the sensory cells of the human eye in the form of hexagonal arrangements.This contribution is therefore concerned with the design, implementation, and evaluation of hexagonal solutions in the form of hexagonal deep neural networks (H-DNN). The realized hexagonal functionality had to be built from the ground up as hexagonal counterparts to otherwise conventional square image processing systems, for which hexagonal equivalents for artificial neural network operations, layers, and models had to be implemented.To enable their evaluation, a set of different application areas within astronomical, medical, and industrial image processing are provided that allow an assessment of H-DNNs in terms of their general performance. The presented results demonstrate the possible benefits of H-DNNs for image processing systems. It is shown that H-DNNs can result in increased classification capabilities given different basic geometric shapes and contours, which in turn partially translate into their real-world applications. Nº de ref. del artículo: 9783961002139

Contactar al vendedor

Comprar nuevo

EUR 32,90
Convertir moneda
Gastos de envío: EUR 11,99
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Tobias Schlosser
ISBN 10: 3961002134 ISBN 13: 9783961002139
Nuevo Taschenbuch

Librería: preigu, Osnabrück, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Taschenbuch. Condición: Neu. Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing | Tobias Schlosser | Taschenbuch | Englisch | Universitätsverlag Chemnitz | EAN 9783961002139 | Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 129323437

Contactar al vendedor

Comprar nuevo

EUR 32,90
Convertir moneda
Gastos de envío: EUR 55,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 5 disponibles

Añadir al carrito