Daniel Keysers

Studied Computer Science in Aachen, Germany and Madrid, Spain; PhD in Computer Science (Image Understanding, Pattern Recognition), RWTH Aachen, Germany; PostDoc at German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Joined Google Zurich in 2007 as Software Engineer, projects: YouTube Content-ID, Handwriting Recognition

Google Publications

Previous Publications

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    Features for image retrieval: an experimental comparison

    Thomas Deselaers, Daniel Keysers, Hermann Ney

    Information Retrieval, vol. 11 (2008), pp. 77-107

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    Deformation models for image recognition

    Daniel Keysers, Thomas Deselaers, Christian Gollan, Hermann Ney

    Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29 (2007), pp. 1422-1435

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    Discriminative Training for Object Recognition using Image Patches

    Thomas Deselaers, Daniel Keysers, Hermann Ney

    IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)

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    Improving a Discriminative Approach to Object Recognition using Image Patches.

    Thomas Deselaers, Daniel Keysers, Hermann Ney

    Pattern Recognition (DAGM) (2005)

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    Adaptation in Statistical Pattern Recognition Using Tangent Vectors

    Daniel Keysers, Wolfgang Macherey, Hermann Ney, Joerg Dahmen

    IEEE Trans. Pattern Analysis Machine Intelligence, vol. 26 (2004), pp. 269-274

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    Elastic image matching is NP-complete

    Daniel Keysers, Walter Unger

    Pattern Recognition Letters, vol. 24 (2003), pp. 445-453

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    Maximum Entropy and Gaussian Models for Image Object Recognition

    Daniel Keysers, Franz Josef Och, Hermann Ney

    DAGM-Symposium (2002), pp. 498-506

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    Improving Automatic Speech Recognition Using Tangent Distance

    Wolfgang Macherey, Daniel Keysers, Joerg Dahmen, Hermann Ney

    European Conference on Speech Communication and Technology (2001)

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    Learning of Variability for Invariant Statistical Pattern Recognition

    Daniel Keysers, Wolfgang Macherey, Joerg Dahmen, Hermann Ney

    European Conference on Machine Learning (ECML) (2001), pp. 263-275