most citedText Detection on Technical Drawings for the Digitization of Brown-field Processes

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20223 cited

Text Detection on Technical Drawings for the Digitization of Brown-field Processes

Tobias Schlagenhauf, Markus Netzer, Jan Hillinger

This paper addresses the issue of autonomously detecting text on technical drawings. The detection of text on technical drawings is a critical step towards autonomous production ma…

cs.CV2020

Siamese Basis Function Networks for Data-efficient Defect Classification in Technical Domains

Tobias Schlagenhauf, Faruk Yildirim, Benedikt Brückner

Training deep learning models in technical domains is often accompanied by the challenge that although the task is clear, insufficient data for training is available. In this work,…

cs.CV2020

A Stitching Algorithm for Automated Surface Inspection of Rotationally Symmetric Components

Tobias Schlagenhauf, Tim Brander, Juergen Fleischer

This paper provides a novel approach to stitching surface images of rotationally symmetric parts. It presents a process pipeline that uses a feature-based stitching approach to cre…

cs.LG2020

GAN based ball screw drive picture database enlargement for failure classification

Tobias Schlagenhauf, Chenwei Sun, Jürgen Fleischer

The lack of reliable large datasets is one of the biggest difficulties of using modern machine learning methods in the field of failure detection in the manufacturing industry. In…

cs.CV2020

Context-based Image Segment Labeling (CBISL)

Tobias Schlagenhauf, Yefeng Xia, Jürgen Fleischer

Working with images, one often faces problems with incomplete or unclear information. Image inpainting can be used to restore missing image regions but focuses, however, on low-lev…