Pramerdorfer C., Kampel M., Van Loock M., “Multi-View Classification and 3D Bounding Box Regression Networks”, International Conference on Pattern Recognition, (to appear), Beijing, China, August 2018.
Pramerdorfer C., Kampel M., “Deep Objective Image Quality Assessment”, 17th international Conference on Computer Analysis of Images and Patterns, pp. 127-138, Ystad, Sweden, August 2017.
Pramerdorfer C., Kampel M., “Facial Expression Recognition using Convolutional Neural Networks: State of the Art“, International Conference on Pattern Recognition (ICPR) Workshops, Cancun, Mexico, December 2016.
Rota P., Sangineto E., Conotter E., Pramerdorfer C., “Bad Teacher or Unruly Student: Can Deep Learning Say Something in Image Forensics Analysis?”, International Conference on Pattern Recognition, pp. 2503-2508, Cancun, Mexico, December 2016.
Pramerdorfer C., Planinc R., Van Loock M., Fankhauser D., Kampel M., Brandstötter M., “Fall Detection Based on Depth-Data in Practice”, European Conference on Computer Vision (ECCV) Workshops, pp. 195-208, Amsterdam, The Netherlands, October 2016.
Pramerdorfer C., Kampel M., “A Dataset for Computer-Vision-Based PCB Analysis”, Proc. 14th IAPR International Conference on Machine Vision Applications, pp. 378-381, Tokyo, Japan, May 2015.
Pramerdorfer C., Kampel M., “PCB Recognition Using Local Features for Recycling Purposes”, Proc. 10th International Conference on Computer Vision Theory and Applications, pp. 71-78, Berlin, Germany, March 2015.
Kleber F., Pramerdorfer C., Wetzinger E., Kampel M., “Optical Sensor Evaluation for Vision Based Recognition of Electronics Waste”, International Journal of Environmental Science and Development, 6(12), pp. 929-933, 2015.
Pramerdorfer C., “Reliable and Practical Human Fall Detection via Depth Data Analysis”, Computer Vision Winter Workshop, Seggau, Austria, February 2015.
Pramerdorfer C., “Depth Data Analysis for Fall Detection”, Master Thesis, Vienna University of Technology, August 2013.
Pramerdorfer C., “Evaluation of Kinect Sensors for Fall Detection”, Proc. IASTED International Conference on Signal Processing, Pattern Recognition and Applications, Innsbruck, Austria, February 2013.