Konvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik
| dc.contributor.author | Mitschke, Norbert | |
| dc.date.accessioned | 2022-08-23T04:02:12Z | |
| dc.date.available | 2022-08-23T04:02:12Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-22T09:17:26Z | |
| dc.identifier | https://library.oapen.org/handle/20.500.12657/58037 | |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/91407 | |
| dc.description.abstract | In the first part of this dissertation, a framework for the design of a CNN for FPGAs is presented, consisting of a preprocessing algorithm, an augmentation technique, a custom quantization scheme and a pruning step of the CNN. The combination of conventional image processing with neural networks is shown in the second part by an example from robotics, where an image-based visual servoing process is successfully conducted for a gripping process of a robot. | |
| dc.language | German | |
| dc.relation.ispartofseries | Forschungsberichte aus der Industriellen Informationstechnik | |
| dc.rights | open access | |
| dc.subject.other | künstliche neuronale Netze; Bildverarbeitung; bildbasierte Regelung; FPGA; CNN; image based visual servoing | |
| dc.subject.other | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering | |
| dc.title | Konvolutionäre neuronale Netze in der industriellen Bildverarbeitung und Robotik | |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000146397 | |
| oapen.relation.isPublishedBy | 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 | |
| oapen.pages | 212 | |
| dc.seriesnumber | 26 |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||

