Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling

Hoppenstedt, Burkhard and Kammerer, Klaus and Reichert, Manfred and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), June 24-27, 2019, Santa Maria al Bagno, Italy.

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In the context of the Industrial Internet of Things (IIoT), image and object recognition has become an important factor. Camera systems provide information to realize sophisticated monitoring applications, quality control solutions, or reliable prediction approaches. During the last years, the evolution of smart glasses has enabled new technical solutions as they can be seen as mobile and ubiquitous cameras. As an important aspect in this context, the recognition of objects from images must be reliably solved to realize the previously mentioned solutions. Therefore, algorithms need to be trained with labeled input to recognize differences in input images. We simplify this labeling process using voice commands in Mixed Reality. The generated input from the mixed- reality labeling is put into a convolutional neural network. The latter is trained to classify the images with different objects. In this work, we describe the development of this mixed-reality prototype with its backend architecture. Furthermore, we test the classification robustness with im- age distortion filters. We validated our approach with format parts from a blister machine provided by a pharmaceutical packaging company in Germany. Our results indicate that the proposed architecture is at least suitable for small classification problems and not sensitive to distortions.

Item Type:Conference or Workshop Item (Paper)
Subjects:DBIS Research > Publications
ID Code:1764
Deposited By: Herr Burkhard Hoppenstedt
BibTex Export:BibTeX
Deposited On:12 Jun 2019 14:00
Last Modified:13 Mar 2020 13:06

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