Publikationsliste für
Daniel Soukup
als Autorin / Autor bzw. wesentlich beteiligte Person

37 Datensätze (2007 - 2019)

Die Publikationen des Centers "Vision, Automation & Control" sind erst ab dem Jahr 2017 vollzählig in der Publikationsdatenbank enthalten. Publikationen aus den Jahren vor 2017 können, müssen aber nicht in der Datenbank vorhanden sein.

In der nachstehenden Liste werden Präsentationen mit Tagungsband doppelt - als Druckpublikation und als Präsentation - ausgegeben. Die Gesamtanzahl der unten aufgelisteten Datensätze ist daher im Allgemeinen größer als die oben angeführte Anzahl.


Bücher und Buch-Herausgaben

1 Datensatz:
  1. Tom Van Der Hor, F. Daubner, S. Stolc, D. Soukup, M. Clabian (Hrg.):
    "Assessment and testing methodologies (PAM4DIS)";
    FRONTEX, Warsaw, Polen, 2019.


Artikel in wissenschaftlichen Zeitschriften

7 Datensätze:
  1. D. Heiss, R. Huber-Mörk, D. Soukup, H. Penz, B. Lopez Garcia:
    "Demosaicing algorithms for area- and line-scan cameras in print inspection";
    Journal of Visual Communication and Image Representation, 20 (2009), 6; S. 389 - 398.

  2. R. Huber-Mörk, M. Löhndorf, D. Heiss, K. Mayer, H. Penz, D. Soukup:
    "Fast and Efficient Colour Inspection using Sets of Ellipsoidal Regions";
    Machine Graphics and Vision, 18 (2009), 3; S. 345 - 361.

  3. D. Soukup, I. Bajla:
    "Robust Object Recognition under Partial Occlusions Using NMF";
    Computational Intelligence and Neuroscience, Vol. 2008 (2008), Article ID 857453; 14 S.

  4. D. Soukup, U. Bodenhofer, M. Mittendorfer-Holzer, K. Mayer:
    "Semiautomatic Identification of Print Layers from a Sequence of Sample Images: A Case Study from Banknote Print Inspection";
    Image and Vision Computing, doi 10.1016 (2008), - ".

  5. D. Soukup, R. Huber-Mörk:
    "Mobile hologram verificaton with deep learning";
    IPSJ Transactions on Computer Vision and Applications, 9:9 (2017), S. 1 - 6.

  6. D. Soukup, P. Thanner:
    "Deep Learning der industriellen Inspektion - KI ist kein Allheilmittel";
    Quality Engineering, P3 (2019), P3/2019; S. 10 - 11.

  7. S. Stolc, D. Soukup, B. Holländer, R. Huber-Mörk:
    "Depth and all-in-focus imaging by a multi-line-scan light-field camera";
    Journal of Electronic Imaging, 23 (2014), 5; S. 1 - 19.


Artikel in technischen Zeitschriften

1 Datensatz:
  1. D. Soukup, P. Thanner:
    "Lessons in training neutral nets";
    IMAGING & machine vision europe, - (2019), Issue 94; S. 28 - 30.


Buchbeiträge

3 Datensätze:
  1. I. Bajla, D. Soukup:
    "Is the Parts-Based Concept of NMF Relevant for Object Recognition Tasks?";
    in: "Machine Learning Research Progress", herausgegeben von: Nova Science Publishers; Nova Science Publishers, Hauppauge, NY, USA, 2011, ISBN: 978-1-60456-646-8, S. 463 - 471.

  2. I. Bajla, D. Soukup, S. Stolc:
    "Occluded Image Object Recognition using Localized Nonnegative Matrix Factorization";
    in: "Object Recognition", K. Lovrecic (Hrg.); herausgegeben von: Intech-Verlag; Intech-Verlag, Rijeka, Kroatien, 2011, ISBN: 978-953-307-222-7, S. 83 - 118.

  3. R. Huber-Mörk, G. Fernández Domínguez, S. Stolc, D. Soukup, C. Beleznai:
    "Inspection Methods for Metal Surfaces: Image Acquisition and Algorithms for the Characterization of Defects";
    in: "Integrated Imaging and Vision Techniques for Industrial Inspection: Advances and Aplications", herausgegeben von: Springer; Springer, London, 2015, ISBN: 978-1-4471-6741-9, S. 55 - 99.


Beiträge in Tagungsbänden

21 Datensätze:
  1. D. Antensteiner, S. Stolc, D. Soukup:
    "Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN";
    Poster: CVPR 2019, Long Beach, California; 16.06.2019 - 20.06.2019; in: "CVPR 2019, Long Beach California", (2019), S. 481 - 483.

  2. I. Bajla, D. Soukup:
    "A modular non-negative matrix factorizatio for parts-based object recognition using subspace representation";
    Vortrag: Electronic Imaging 2008, San Jose; 27.01.2008 - 31.01.2008; in: "Electronic Imaging 2008 "Image Processing: Machine Vision Applications" (Volume 6813)", SPIE, (2008), ISSN: 0277-786x; S. 68130C-1 - 68130C-9.

  3. I. Bajla, D. Soukup:
    "Non-Negative matrix factorization: a study on influence of matrix sparseness and subspace distance metrics on image object recognition";
    Vortrag: Quality Control by Artificial Vision 2007, Le Creusot, Frankreich; 23.05.2007 - 26.05.2007; in: "SPIE Proceedings "Quality Control by Artificial Vision"", SPE, Vol. 6356 (2007), ISBN: 9780819464514; S. 635614-1 - 635614-12.

  4. B. Blaschitz, D. Soukup, H. Penz, W. Krattenthaler, R. Huber-Mörk:
    "Testing a Banknote Checking System";
    Vortrag: 28th International Conference on Testing Software and Systems (ICTSS), Graz; 17.10.2016 - 19.10.2016; in: "Joint Proceedings of the International Workshop on Quality Assurance in Computer Vision and the International Workshop on Digital Eco-Systems", CEUR-WS, Aachen (2016), ISSN: 1613-0073; 6 S.

  5. R. Huber-Mörk, D. Soukup:
    "Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 668 - 677.

  6. R. Huber-Mörk, D. Soukup:
    "Image Super-Resolution for Line Scan Cameras based on a Time Delay Super-Resolution Principle";
    Vortrag: 6th International Symposium on Image and Signal Processing and Analysis, Salzburg; 16.09.2009 - 18.09.2009; in: "Conference Proceedings of ISPA 2009", IEEE, (2009), ISSN: 1845-5956; 6 S.

  7. R. Huber-Mörk, D. Soukup, S. Stolc, B. Holländer:
    "Depth Estimation within a Multi-Line-Scan Light-Field Framework";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 471 - 481.

  8. R. Huber-Mörk, S. Stolc, D. Soukup, B. Holländer:
    "Shape from Refocus";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 153 - 162.

  9. T. Pinetz, D. Soukup, R. Huber-Mörk, R. Sablatnig:
    "Using a U-Shaped Neural Network for minutiae extraction trained from refined, synthetic fingerprints";
    Vortrag: Proceedings of the ÖAGM & ARW Joint Workshop 2017, Vision Automation and Robotics, Wien; 10.05.2017 - 12.05.2017; in: "Proceedings of the ÖAGM & ARW Joint Workshop 2017, Vision Automation and Robotics", Austrian Association for Pattern Recognition and Austrian Robotics Workshop, Verlag der Technischen Universität Graz, Graz, (2017), ISBN: 978-3-85125-524-9; S. 146 - 151.

  10. T. Pinetz, D. Soukup, T. Pock:
    "On the Estimation of the Wasserstein Distance in Generative Models";
    Vortrag: 41st DAGM GCPR 2019, Dortmund, Deutschland; 10.09.2019 - 13.09.2019; in: "Pattern Recognition, Proceedings of 41st DAGM German Conference, DAGM GCPR 2019", (2019), ISBN: 978-3-030-33676-9; S. 156 - 170.

  11. T. Pinetz, D. Soukup, T. Pock:
    "What is optimized in Wasserstein GANs?";
    Hauptvortrag: Computer Vision Winter Workshop 2018, Ceský Krumlov, Cezch Republic; 05.02.2018 - 07.02.2018; in: "Proceedings of the 23rd Computer Vision Winter Workshop", Czech, Vl. 23, Paper-Nummer: 16 (2018), ISBN: 978-80-270-3395-9; Paper-Nr. 16, 9 S.

  12. T. Pinetz, D. Soukup, J. Ruisz:
    "Actual impact of GAN augmentation on CNN classification performance";
    Vortrag: International Conference on Pattern Recognition Applications and Methods, Prague; 19.02.2019 - 21.02.2019; in: "Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods", Vol. 1, Paper-Nr. 2 (2019), ISBN: 978-989-758-351-3; S. 15 - 23.

  13. D. Soukup, R. Huber-Mörk:
    "Cross-Channel Co-Occurence Matrices for Robust Characterization of Surface Disruptions in 2 1/2 D Rail Image Analysis";
    Vortrag: Advanced Concepts for Intelligent Vision Systems, Brno, CZ; 04.09.2012 - 07.09.2012; in: "Proceedings of Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science", Springer, Berlin (2012), ISBN: 978-3-642-17687-6; S. 469 - 474.

  14. D. Soukup, R. Huber-Mörk:
    "Mobile Hologram Verification With Deep Learning";
    Vortrag: IAPR Int. Conference on Machine Vision and Applications (MVA) 2017, MVA Organization, Japan; 08.05.2017 - 12.05.2017; in: "Proceedings of MVA2017", 5-02 (2017), S. 1 - 6.

  15. D. Soukup, R. Huber-Mörk:
    "Mobile Hologram Verification With Deep Learning";
    Poster: IAPR Int. Confrence on Machine Vision and Applications (MVA) 2017, MVA Organization, Japan; 08.05.2017 - 12.05.2017; in: "Proceedings of MVA 2017", 5-02 (2017), S. 1 - 6.

  16. D. Soukup, T. Klatzer, Erich Kobler, K. Hammernik, T. Pock:
    "Trainable Regularization for Multi-frame Superresolution";
    Poster: German Conference on Pattern Recognition (GCPR) 2017, Basel, Switzerland; 13.09.2017 - 15.09.2017; in: "Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science", Springer, Cham, Vol. 10496 (2017), ISBN: 978-3-319-66708-9; S. 90 - 100.

  17. D. Soukup, T. Pinetz:
    "Reliably Decoding Autoencoders´Latent Spaces for One-Class-Learning Image Inspection Scenarios";
    Vortrag: OEAGM Workshop 2018, Hall/Tirol; 15.05.2018 - 16.05.2018; in: "Proceedings of the OAGM Workshop 2018, Medical Image Analysis", Peter Roth, Martin Welk, Martin Urschler; Verlag der Technischen Universität Graz, Graz (2018), ISBN: 978-3-85125-603-1; S. 90 - 93.

  18. D. Soukup, S. Stolc, R. Huber-Mörk:
    "On optimal illumination for DOVID description using photometric stereo";
    Vortrag: Advanced Concepts for Intelligent Vision Systems, ACIVS 2015, Catania, IT; 26.10.2015 - 29.10.2015; in: "Proceedings of Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science", Springer, LNCS 7517 (2015), ISBN: 978-3-642-17687-6; Paper-Nr. LNCS 9386, 23 S.

  19. S. Stolc, R. Huber-Mörk, B. Holländer, D. Soukup:
    "Depth and all-in-focus images obtained by multi-line-scan light-field approach";
    Vortrag: SPIE-IS&T Electronic Imaging - Image Processing: Machine Vision Applications VII, San Francisco, CA, USA; 02.02.2014 - 06.02.2014; in: "Proceedings of SPIE-IS&T Electronic Imaging - Image Processing: Machine Vision Applications VII", SPIE, Bellingham (2014), ISBN: 978-0-8194-9941-7; 16 S.

  20. S. Stolc, D. Soukup, R. Huber-Mörk:
    "Analysis of optically variable devices using a photometric light-field approach";
    Vortrag: IS&T/SPIE Electronic Imaging 2015 - Media Watermarking, Security, and Forensics 2015, San Francisco, CA; 08.02.2015 - 12.02.2015; in: "Media Watermarking, Security and Forensics 2015", SPIE-IS&T, (2015), ISBN: 9781628414998; Paper-Nr. 94090R, 9 S.

  21. S. Stolc, D. Soukup, R. Huber-Mörk:
    "Invariant characterization of dovid security features using a photometric descriptor";
    Vortrag: IEEE International Conference on Image Processing, Quebec City, Canada; 27.09.2015 - 30.09.2015; in: "Proceedings of the International Conference on Image Processing", IEEE, (2015), ISBN: 978-1-4244-7994-8; Paper-Nr. IFS-P1.3, 5 S.


Vorträge und Posterpräsentationen auf Tagungen

24 Datensätze:
  1. D. Antensteiner, S. Stolc, D. Soukup:
    "Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN";
    Poster: CVPR 2019, Long Beach, California; 16.06.2019 - 20.06.2019; in: "CVPR 2019, Long Beach California", (2019), S. 481 - 483.

  2. D. Antensteiner, S. Stolc, D. Soukup:
    "Surface Normals and Albedo Reconstruction from Single-Shot Images Using a Split U-Net";
    Vortrag: EMVA Forum 2019, Lyon, France; 04.09.2019 - 06.09.2019.

  3. I. Bajla, D. Soukup:
    "A modular non-negative matrix factorizatio for parts-based object recognition using subspace representation";
    Vortrag: Electronic Imaging 2008, San Jose; 27.01.2008 - 31.01.2008; in: "Electronic Imaging 2008 "Image Processing: Machine Vision Applications" (Volume 6813)", SPIE, (2008), ISSN: 0277-786x; S. 68130C-1 - 68130C-9.

  4. I. Bajla, D. Soukup:
    "Non-Negative matrix factorization: a study on influence of matrix sparseness and subspace distance metrics on image object recognition";
    Vortrag: Quality Control by Artificial Vision 2007, Le Creusot, Frankreich; 23.05.2007 - 26.05.2007; in: "SPIE Proceedings "Quality Control by Artificial Vision"", SPE, Vol. 6356 (2007), ISBN: 9780819464514; S. 635614-1 - 635614-12.

  5. B. Blaschitz, D. Soukup, H. Penz, W. Krattenthaler, R. Huber-Mörk:
    "Testing a Banknote Checking System";
    Vortrag: 28th International Conference on Testing Software and Systems (ICTSS), Graz; 17.10.2016 - 19.10.2016; in: "Joint Proceedings of the International Workshop on Quality Assurance in Computer Vision and the International Workshop on Digital Eco-Systems", CEUR-WS, Aachen (2016), ISSN: 1613-0073; 6 S.

  6. R. Huber-Mörk, D. Soukup:
    "Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 668 - 677.

  7. R. Huber-Mörk, D. Soukup:
    "Image Super-Resolution for Line Scan Cameras based on a Time Delay Super-Resolution Principle";
    Vortrag: 6th International Symposium on Image and Signal Processing and Analysis, Salzburg; 16.09.2009 - 18.09.2009; in: "Conference Proceedings of ISPA 2009", IEEE, (2009), ISSN: 1845-5956; 6 S.

  8. R. Huber-Mörk, D. Soukup, S. Stolc, B. Holländer:
    "Depth Estimation within a Multi-Line-Scan Light-Field Framework";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 471 - 481.

  9. R. Huber-Mörk, S. Stolc, D. Soukup, B. Holländer:
    "Shape from Refocus";
    Vortrag: 10th International Symposium on Visual Computing, Las Vegas, NV, USA; 08.12.2014 - 10.12.2014; in: "Advances in Visual Computing, Part 1, Lecture Notes in Computer Science", Springeer, LNCS 8887, Heidelberg (2014), ISBN: 978-3-319-14248-7; S. 153 - 162.

  10. T. Pinetz, D. Soukup, R. Huber-Mörk, R. Sablatnig:
    "Using a U-Shaped Neural Network for minutiae extraction trained from refined, synthetic fingerprints";
    Vortrag: Proceedings of the ÖAGM & ARW Joint Workshop 2017, Vision Automation and Robotics, Wien; 10.05.2017 - 12.05.2017; in: "Proceedings of the ÖAGM & ARW Joint Workshop 2017, Vision Automation and Robotics", Austrian Association for Pattern Recognition and Austrian Robotics Workshop, Verlag der Technischen Universität Graz, Graz, (2017), ISBN: 978-3-85125-524-9; S. 146 - 151.

  11. T. Pinetz, D. Soukup, T. Pock:
    "On the Estimation of the Wasserstein Distance in Generative Models";
    Vortrag: 41st DAGM GCPR 2019, Dortmund, Deutschland; 10.09.2019 - 13.09.2019; in: "Pattern Recognition, Proceedings of 41st DAGM German Conference, DAGM GCPR 2019", (2019), ISBN: 978-3-030-33676-9; S. 156 - 170.

  12. T. Pinetz, D. Soukup, T. Pock:
    "What is optimized in Wasserstein GANs?";
    Hauptvortrag: Computer Vision Winter Workshop 2018, Ceský Krumlov, Cezch Republic; 05.02.2018 - 07.02.2018; in: "Proceedings of the 23rd Computer Vision Winter Workshop", Czech, Vl. 23, Paper-Nummer: 16 (2018), ISBN: 978-80-270-3395-9; Paper-Nr. 16, 9 S.

  13. T. Pinetz, D. Soukup, J. Ruisz:
    "Actual impact of GAN augmentation on CNN classification performance";
    Vortrag: International Conference on Pattern Recognition Applications and Methods, Prague; 19.02.2019 - 21.02.2019; in: "Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods", Vol. 1, Paper-Nr. 2 (2019), ISBN: 978-989-758-351-3; S. 15 - 23.

  14. D. Soukup, D. Antensteiner, K. Valentin, R. Huber-Mörk, S. Stolc:
    "High Speed Multi-Line Scan Imaging für Inspection of Tactile Security Features";
    Vortrag: 2016 Banknote Equipment Manufacturer Conference, Charlotte, USA (eingeladen); 21.09.2016 - 22.09.2016.

  15. D. Soukup, R. Huber-Mörk:
    "Cross-Channel Co-Occurence Matrices for Robust Characterization of Surface Disruptions in 2 1/2 D Rail Image Analysis";
    Vortrag: Advanced Concepts for Intelligent Vision Systems, Brno, CZ; 04.09.2012 - 07.09.2012; in: "Proceedings of Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science", Springer, Berlin (2012), ISBN: 978-3-642-17687-6; S. 469 - 474.

  16. D. Soukup, R. Huber-Mörk:
    "Mobile Hologram Verification With Deep Learning";
    Vortrag: IAPR Int. Conference on Machine Vision and Applications (MVA) 2017, MVA Organization, Japan; 08.05.2017 - 12.05.2017; in: "Proceedings of MVA2017", 5-02 (2017), S. 1 - 6.

  17. D. Soukup, R. Huber-Mörk:
    "Mobile Hologram Verification With Deep Learning";
    Poster: IAPR Int. Confrence on Machine Vision and Applications (MVA) 2017, MVA Organization, Japan; 08.05.2017 - 12.05.2017; in: "Proceedings of MVA 2017", 5-02 (2017), S. 1 - 6.

  18. D. Soukup, T. Klatzer, Erich Kobler, K. Hammernik, T. Pock:
    "Trainable Regularization for Multi-frame Superresolution";
    Poster: German Conference on Pattern Recognition (GCPR) 2017, Basel, Switzerland; 13.09.2017 - 15.09.2017; in: "Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science", Springer, Cham, Vol. 10496 (2017), ISBN: 978-3-319-66708-9; S. 90 - 100.

  19. D. Soukup, T. Pinetz:
    "Reliably Decoding Autoencoders´Latent Spaces for One-Class-Learning Image Inspection Scenarios";
    Vortrag: OEAGM Workshop 2018, Hall/Tirol; 15.05.2018 - 16.05.2018; in: "Proceedings of the OAGM Workshop 2018, Medical Image Analysis", Peter Roth, Martin Welk, Martin Urschler; Verlag der Technischen Universität Graz, Graz (2018), ISBN: 978-3-85125-603-1; S. 90 - 93.

  20. D. Soukup, S. Stolc, R. Huber-Mörk:
    "On optimal illumination for DOVID description using photometric stereo";
    Vortrag: Advanced Concepts for Intelligent Vision Systems, ACIVS 2015, Catania, IT; 26.10.2015 - 29.10.2015; in: "Proceedings of Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science", Springer, LNCS 7517 (2015), ISBN: 978-3-642-17687-6; Paper-Nr. LNCS 9386, 23 S.

  21. D. Soukup, S. Stolc, P. Thanner:
    "Deep learning as Substitute for CRF-Regularization in 3D Image Processing";
    Vortrag: EMVA Forum 2018, Bologna, Italien; 05.09.2018 - 07.09.2018.

  22. S. Stolc, R. Huber-Mörk, B. Holländer, D. Soukup:
    "Depth and all-in-focus images obtained by multi-line-scan light-field approach";
    Vortrag: SPIE-IS&T Electronic Imaging - Image Processing: Machine Vision Applications VII, San Francisco, CA, USA; 02.02.2014 - 06.02.2014; in: "Proceedings of SPIE-IS&T Electronic Imaging - Image Processing: Machine Vision Applications VII", SPIE, Bellingham (2014), ISBN: 978-0-8194-9941-7; 16 S.

  23. S. Stolc, D. Soukup, R. Huber-Mörk:
    "Analysis of optically variable devices using a photometric light-field approach";
    Vortrag: IS&T/SPIE Electronic Imaging 2015 - Media Watermarking, Security, and Forensics 2015, San Francisco, CA; 08.02.2015 - 12.02.2015; in: "Media Watermarking, Security and Forensics 2015", SPIE-IS&T, (2015), ISBN: 9781628414998; Paper-Nr. 94090R, 9 S.

  24. S. Stolc, D. Soukup, R. Huber-Mörk:
    "Invariant characterization of dovid security features using a photometric descriptor";
    Vortrag: IEEE International Conference on Image Processing, Quebec City, Canada; 27.09.2015 - 30.09.2015; in: "Proceedings of the International Conference on Image Processing", IEEE, (2015), ISBN: 978-1-4244-7994-8; Paper-Nr. IFS-P1.3, 5 S.


Sonstige Vorträge und Posterpräsentationen

1 Datensatz:
  1. D. Soukup, S. Stolc, R. Huber-Mörk:
    "Analysis of optically variable devices using a photometric light-field approach";
    Vortrag: Vortrag an der TU Graz, Graz; 02.06.2015.