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Member Profile

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.
 Portrait Photo  
Firstname:  Eduardo
Lastname:  Ribeiro
Room No: 0.29
Telephone: (+43) 662 8044 - 6300
Mail:  uft [dot] eduardo [at] gmail [dot] com

Eduardo Ribeiro


About Me:
I am a PhD student in the WaveLab group.


Research Interests:

  • Deep Learning
  • Superresolution



    Journal Articles

    1. [Ribeiro16c  ] Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification E. Ribeiro, A. Uhl, G. Wimmer, M. Häfner Computational and Mathematical Methods in Medicine 2016, pp. Article ID 6584725, Hindawi Publishing Corporation, 2016

    Conference Papers

    1. [Ribeiro16d  ] Exploring Texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks E. Ribeiro, M. Häfner, G. Wimmer, T. Tamaki, J.J.W. Tischendorf, S. Yoshida, S. Tanaka, A. Uhl In 14th International IEEE Symposium on Biomedical Imaging (ISBI'17)April 2017
    2. [Ribeiro17b  ] Exploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolution Eduardo Ribeiro, Andreas Uhl In Arslan Brömme, Christoph Busch, Christian Rathgeb, Andreas Uhl, editors, Proceedings of the 2017 International Conference of the Biometrics Special Interest Group (BIOSIG'17), Darmstadt, Germany 2017, LNI, GI / IEEE, 2017
    3. [Ribeiro17a  ] Exploring Deep Learning Image Super-Resolution for Iris Recognition Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez, Reuben A. Farrugia In Proc. of the 25th European Signal Processing Conference (EUSIPCO 2017), Kos Island, Greece, August 28 - September 2, 20172017
    4. [Ribeiro16b ] Transfer Learning for Colonic Polyp Classification using Off-the-Shelf CNN Features (Best Paper Award, 3rd Place) E. Ribeiro, A. Uhl, G. Wimmer, M. Häfner In Proceedings of the 3rd International Workshop on Computer-Assisted and Robotic Endoscopy (CARE'16), pp. 1-13, Springer LNCS, 10170, 2016
    5. [Ribeiro16a  ] Colonic Polyp Classification with Convolutional Neural Networks Eduardo Ribeiro, Andreas Uhl, Michael Häfner In Proceedings of the 29th IEEE International Symposium on Computer-Based Medical Systems (CBMS'16), pp. 253-258, June 2016