Articles liés à Biologically Inspired Hexagonal Deep Learning for Hexagonal...
- ISBN 10 3961002134
- ISBN 13 9783961002139
- ReliureCopertina flessibile
- Langueanglais
- Nombre de pages300
- Coordonnées du fabricantnon disponible
Acheter neuf
Afficher cet article
EUR 32,90
EUR 10,99 expédition depuis Allemagne vers France
Destinations, frais et délaisRésultats de recherche pour Biologically Inspired Hexagonal Deep Learning for Hexagonal...
Image fournie par le vendeur
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing : DE
Edité par
Technische Universität Chemnitz
ISBN 10 : 3961002134
ISBN 13 : 9783961002139
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783961002139
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing
Edité par
Technische Universität Chemnitz
ISBN 10 : 3961002134
ISBN 13 : 9783961002139
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783961002139
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing
Edité par
Universitätsverlag Chemnitz, 2024
ISBN 10 : 3961002134
ISBN 13 : 9783961002139
Neuf
Taschenbuch
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 129323437
Quantité disponible : 5 disponible(s)