Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing

Name: Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing
Brand: Technische Universität Chemnitz
SKU: 9783961002139
Price: 12013 HUF
Availability: InStock

Szerző: Tobias Schlosser

Nyelv:

Kötés: Puha kötésű

Kiadó: Technische Universität Chemnitz

Elérhetőség: Kiadói készleten rendelésre

Küldés 17-27 napon belül

12 013 Ft

While current approaches to digital image processing in the context of deep learning are motivated b...

Információk a könyvről

Szerző

Tobias Schlosser

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2024

oldal

300

EAN

9783961002139

ISBN

3961002134

Enbook ID

46067565

Kiadó

Technische Universität Chemnitz

Súly

438

Méretek

148 x 210 x 19

Teljes leírás

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.

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Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing

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