Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
International
Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics
Page - 145 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 145 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics

Image of the Page - 145 -

Image of the Page - 145 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics

Text of the Page - 145 -

[17] C. Payer, D. Sˇtern, H. Bischof, and M. Urschler, “Regressing Heatmaps for Multiple Landmark Localization Using CNNs,” in In- ternational Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2016, pp. 230–238. [18] A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” in International Conference on Learning Representations, 2016. [19] A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition,” in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, ser. CVPRW ’14. Washington, DC, USA: IEEE Computer Society, 2014, pp. 512–519. [Online]. Available: http://dx.doi.org/10.1109/CVPRW.2014.131 [20] G. Riegler, M. Urschler, M. Ru¨ther, H. Bischof, and D. Sˇtern, “Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data,” in 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Dec 2015, pp. 85–89. [21] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), ser. LNCS, vol. 9351. Springer, 2015, pp. 234–241, (available on arXiv:1505.04597 [cs.CV]). [Online]. Available: http://lmb.informatik.uni-freiburg.de//Publications/2015/RFB15a [22] A. Rozantsev, V. Lepetit, and P. Fua, “On Rendering Synthetic Images for Training an Object Detector,” Computer Vision and Image Understanding, vol. 137, pp. 24 – 37, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1077314214002446 [23] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211–252, 2015. [24] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen, “Improved Techniques for Training GANs,” in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 2234– 2242. [Online]. Available: http://papers.nips.cc/paper/6125-improved- techniques-for-training-gans.pdf [25] J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K.-i. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi, “Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule,” American Journal of Roentgenology, vol. 174, no. 1, pp. 71–74, Jan. 2000. [Online]. Available: http://dx.doi.org/10.2214/ajr.174.1.1740071 [26] B. van Ginneken, M. Stegmann, and M. Loog, “Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database,” Medical Image Analysis, vol. 10, no. 1, pp. 19–40, 2006. [27] P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and Composing Robust Features with Denoising Autoencoders,” in Proceedings of the 25th International Conference on Machine Learning, ser. ICML ’08. New York, NY, USA: ACM, 2008, pp. 1096–1103. [Online]. Available: http://doi.acm.org/10.1145/1390156.1390294 [28] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 3320–3328. [Online]. Available: http://papers.nips.cc/paper/5347- how-transferable-are-features-in-deep-neural-networks.pdf 145
back to the  book Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics"
Proceedings of the OAGM&ARW Joint Workshop Vision, Automation and Robotics
Title
Proceedings of the OAGM&ARW Joint Workshop
Subtitle
Vision, Automation and Robotics
Authors
Peter M. Roth
Markus Vincze
Wilfried Kubinger
Andreas MĂĽller
Bernhard Blaschitz
Svorad Stolc
Publisher
Verlag der Technischen Universität Graz
Location
Wien
Date
2017
Language
English
License
CC BY 4.0
ISBN
978-3-85125-524-9
Size
21.0 x 29.7 cm
Pages
188
Keywords
Tagungsband
Categories
International
Tagungsbände

Table of contents

  1. Preface v
  2. Workshop Organization vi
  3. Program Committee OAGM vii
  4. Program Committee ARW viii
  5. Awards 2016 ix
  6. Index of Authors x
  7. Keynote Talks
  8. Austrian Robotics Workshop 4
  9. OAGM Workshop 86
Web-Books
Library
Privacy
Imprint
Austria-Forum
Austria-Forum
Web-Books
Proceedings of the OAGM&ARW Joint Workshop