#Computer Vision

Computer Vision deals with the automatic recognition, processing and understanding of digital visual content such as images or videos

Projects

Scribe ID AI

Active Machine Learning for automatic identification of handwriting in 12th century manuscripts

Digital Makers Hub

Platform for digital makers, for interaction, efficient sharing of knowledge & experiences and nationwide coordination of activities

ReMoCap-Lab

The „Laboratory for Capturing Motion and Augmenting Environment in Motor Rehabilitation“ will enable innovative projects and excellent research in Austria

Ressel Center: Horizons of personalized music therapy 2

Exploration of music therapeutic processes and relationships in selected areas of neurological rehabilitation

ImmBild - Location Assessment by Computer Vision

Location is key – especially when it comes to real estate value. “ImmBild” aims at developing a new method for estimating property value using computer vision of satellite data.

WiKant-Knowledge-based production of profiles

Optimising production through automation of the entire process chain

MEETeuX

Multi-Device Ecologies Towards Elaborate User Experience discusses issues of relevance regarding interaction design and user experience design and aims at an integrated use of media technology devices...

360 AI

Developing a method for efficient object recognition in 360° images

InfraBase - Automatic Building Footprint Segmentation

The project deals with the fully automatic analysis of satellite imagery. The goal is to extract a pixel-accurate segmentation of building roofs to generate a rich meta-data layer.

Object Recognition for Indoor Navigation

Object recognition for autonomous indoor positioning and navigation

Publications

Horsak, B. (2019). Reliabilität von Messergebnissen in der Gang- und Bewegungsanalyse – Erfahrungsbericht zu gängigen Maßzahlen. Invited talk presented at the GAMMA Workshop im Rahmen des 11. Kongress der Deutschen Gesellschaft für Biomechanik, Berlin.
Blumenstein, K., Oliveira, V. A. D. J., Größbacher, S., Boucher, M., Seidl, M., & Aigner, W. (2019). Design of Time-Oriented Visualization for Mobile Applications in Museums: A Comparative Evaluation. In Adjunct Proceedings CHItaly19 (p. 5). Padova, Italy.
Seidl, Markus, & Zeppelzauer, Matthias. (2019). Towards Distinction of Rock Art Pecking Styles with a Hybrid 2D/3D Approach. In Proceedings of the International Conference on Content-based Multimedia Indexing (CBMI) (p. 4). Dublin, Irland.
Despotovic, M., Koch, D., Leiber, S., Döller, M., Sakeena, M., & Zeppelzauer, M. (2019). Prediction and Analysis of Heating Energy Demand and Year of Construction for Detached Houses by Computer Vision. Energy & Buildings, 193, 29–35. https://doi.org/10.1016/j.enbuild.2019.03.036
Rind, A., Wagner, M., & Aigner, W. (2019). Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics. In Proc. Workshop on Visual Analytics in Healthcare (VAHC 2019).
Zeppelzauer, M. (2018). Automatic Prediction of Building Age from Photographs. Yokohama, Japan.
Koch, D., Despotovic, M., Sascha, L., Sakeena, M., Döller, M., & Zeppelzauer, M. (2018). Real Estate Image Analysis - A Literature Review. Real Estate Economics Journal, to Appear.
Koch, D., Despotovic, M., Sakeena, M., Döller, M., & Zeppelzauer, M. (2018). Visual Estimation of Building Condition with Patch-level ConvNets. In Proceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech - RETech"18 (pp. 12–17). Yokohama, Japan: ACM Press. https://doi.org/10.1145/3210499.3210526
Koch, D., Despotovic, M., Sakeena, M., Döller, M., & Zeppelzauer, M. (2018). Visual Estimation of Building Condition with Patch-level ConvNets. Yokohama, Japan.
Zeppelzauer, M., Zielinski, B., Juda, M., & Seidl, M. (2018). A Study on Topological Descriptors for the Analysis of 3D Surface Texture. Journal on Computer Vision and Image Understanding (CVIU), 167, 74–88. https://doi.org/10.1016/j.cviu.2017.10.012

News

11/19/2019

Traceing the Writers of the Middle Ages