Image Processing Using A Convolutional Neural Network C. Schroers, F. Perazzi, and C. Hazirbas US Patent App. 15/919,715, September 2019 [ bib | doi ] |
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Deep Depth From Focus C. Hazirbas, S. G. Soyer, M. C. Staab, L. Leal-Taixé, and D. Cremers ACCV, December 2018 [ bib | arXiv | source ] |
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What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? N. Mayer, E. Ilg, P. Fischer, C. Hazirbas, D. Cremers, A. Dosovitskiy, and T. Brox IJCV, January 2018 [ bib | arXiv ] |
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Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging
Problems T. Meinhardt, M. Möller, C. Hazirbas, and D. Cremers ICCV, October 2017 [ bib | arXiv | source ] |
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Image-based localization using LSTMs for structured feature correlation F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck, and D. Cremers ICCV, October 2017 [ bib | arXiv | source ] |
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FuseNet:Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture C. Hazirbas, L. Ma, C. Domokos, and D. Cremers ACCV, November 2016 [ bib | doi | source ] |
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FlowNet: Learning Optical Flow with Convolutional Networks A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox ICCV, December 2015 [ bib | arXiv | doi ] |
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CAPTCHA Recognition with Active Deep Learning F. Stark, C. Hazirbas, R. Triebel, and D. Cremers GCPRW, October 2015 [ bib | source ] |
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Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation C. Hazirbas, J. Diebold, and D. Cremers SSVM, June 2015 Oral Presentation [ bib | doi | source ] |
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Interactive Multi-label Segmentation of RGB-D Images J. Diebold, N. Demmel, C. Hazirbas, M. Möller, and D. Cremers SSVM, June 2015 [ bib | doi | source ] |
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Feature Selection and Learning for Semantic Segmentation C. Hazirbas Master's thesis, Technical University Munich, Germany, June 2014 [ bib ] |