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Daniel Freedman

PRINCIPAL APPLIED RESEARCHER
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Professor Daniel Freedman is Principal Applied Researcher at Microsoft Research - Advanced Technology Labs Israel.  His research interests are in the areas of computer vision, machine learning, and computational topology and geometry.  At Microsoft, Professor Freedman has worked on problems and technologies at the intersection of 3D sensing, signal processing, and machine learning. His research uses a wide variety of techniques from mathematics, including algebraic topology, partial differential equations, and combinatorial optimization.

Prior to Microsoft, Professor Freedman moved between academia and industrial research, including a 9 year stint at RPI as Assistant Professor, and then Associate Professor (with tenure) of Computer Science.

Curriculum Vitae

The full CV may be found here.

Publications

D. Freedman, Y. Smolin, E. Krupka, I. Leichter, and M. Schmidt, SRA: Fast Removal of General Multipath for ToF Sensors, in Proceeedings of the European Conference on Computer Vision (ECCV), September 2014

E. Krupka, A. Vinnikov, B. Klein, A. Bar-Hillel, D. Freedman, S. Stachniak, and C. Keskin, Learning Fast Hand Pose Recognition, in Computer Vision and Machine Learning with RGB-D Sensors, pp. 267–288, Springer, July 2014

E. Krupka, A. Vinnikov, B. Klein, A. Bar-Hillel, D. Freedman, and S. Stachniak, Discriminative Ferns Ensemble for Hand Pose Recognition, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014

P. Kisilev, D. Freedman, E. Walach, and A. Tzadok, DFlow and DField: New Features for Capturing Object and Image Relationships, in Proceeedings of the International Conference on Pattern Recognition (ICPR), 2012

A. Dubrovina, P. Kisilev, D. Freedman, S. Schein, and R. Bergman, Efficient and Robust Image Descriptor for GUI Object Classification, in Proceeedings of the International Conference on Pattern Recognition (ICPR), 2012

C. Chen and D. Freedman, Topology noise removal for curve and surface evolution, in Medical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging – Springer Lecture Notes in Computer Science (vol 6533), pp. 31–42, Springer, 2011

D. Freedman and C. Chen, Algebraic topology for computer vision, in Computer Vision, pp. 239–268, Nova Science, 2011

C. Chen and D. Freedman, Hardness results for optimal homology bases, in Discrete and Computational Geometry, vol. 45, no. 3, pp. 425–448, 2011

C. Chen, D. Freedman, and C.H. Lampert, Enforcing topological constraints in random field image segmentation, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

C. Chen and D. Freedman, Hardness results for homology localization, in Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010

P. Kisilev and D. Freedman, Parameter tuning by pairwise preferences, in Proceedings of the British Machine Vision Conference (BMVC), 2010

D. Freedman and P. Kisilev, Object-to-object color transfer: optimal flows and SMSP transformations, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

D. Freedman and P. Kisilev, KDE Paring and a Faster Mean Shift Algorithm, in SIAM Journal on Imaging Sciences, vol. 3, no. 4, pp. 878–903, 2010

D. Freedman and M.W. Turek, Graph cuts with many-pixel interactions: theory and applications to shape modelling, in Image and Vision Computing, vol. 28, no. 3, pp. 467–473, 2010

Z. Karni, D. Freedman, and D. Shaked, Fast inverse halftoning, in Proceeedings of the 31st International Congress on Imaging Science (ICIS), 2010

D. Freedman, An improved image graph for semi-automatic segmentation, in Signal, Image and Video Processing, pp. 1–13, 2010

C. Chen and D. Freedman, Measuring and computing natural generators for homology groups, in Computational Geometry: Theory and Applications, vol. 43, no. 2, pp. 169–181, 2010

D. Freedman and P. Kisilev, Computing Color Transforms with Applications to Image Editing, in Journal of Mathematical Imaging and Vision, vol. 37, no. 3, pp. 220–231, 2010

R. Chen, D. Freedman, Z. Karni, C. Gotsman, and L. Liu, Content-aware image resizing by quadratic programming, in Proceedings of the Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA) (in conjunction with CVPR), 2010

P. Kisilev and D. Freedman, Color transforms for creative image editing, in Proceedings of the Seventeenth IS&T Color Imaging Conference (CIC), 2009

D. Freedman and P. Kisilev, Fast mean shift by compact density representation, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009

D. Freedman and P. Kisilev, Fast data reduction via KDE approximation, in Proceedings of the Data Compression Conference (DCC), 2009

Z. Karni, D. Freedman, and C. Gotsman, Energy-Based Image Deformation, in Computer Graphics Forum, vol. 28, no. 5, pp. 1257–1268, 2009

Z. Karni, D. Freedman, and C. Gotsman, Energy-based shape deformation, in Proceedings of the ACM Symposium on Geometry Processing (SGP), 2009

C. Chen and D. Freedman, Quantifying homology classes, in Proceedings of the International Symposium on Theoretical Aspects of Computer Science (STACS), 2008

A. Ayvaci and D. Freedman, Joint segmentation-registration of organs using geometric models, in Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2007

D. Freedman, An incremental algorithm for reconstruction of surfaces of arbitrary codimension, in Computational Geometry: Theory and Applications, vol. 36, no. 2, pp. 106–116, 2007

M.W. Turek and D. Freedman, Multiscale Modeling and Constraints for Max-flow/Min-cut Problems in Computer Vision, in Proceedings of the IEEE Workshop on Perceptual Organization in Computer Vision (in conjunction with CVPR), 2006

D. Freedman and T. Zhang, Interactive graph cut based segmentation with shape priors, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005

D. Freedman, R.J. Radke, T. Zhang, Y. Jeong, D.M. Lovelock, and G.T.Y. Chen, Model-based segmentation of medical imagery by matching distributions, in IEEE Transactions on Medical Imaging, vol. 24, no. 3, pp. 281–292, 2005

D. Freedman and P. Drineas, Energy minimization via graph cuts: Settling what is possible, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005

D. Freedman and M.W. Turek, Illumination-invariant tracking via graph cuts, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005

T. Zhang and D. Freedman, Improving performance of distribution tracking through background mismatch, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 282–287, 2005

D. Freedman and T. Zhang, Active contours for tracking distributions, in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 518–526, 2004

D. Freedman, R.J. Radke, T. Zhang, Y. Jeong, and G.T.Y. Chen, Model-based multi-object segmentation via distribution matching, in Proceedings of the IEEE Workshop on Articulated and Nonrigid Motion (in conjunction with CVPR), 2004

R.J. Radke, D. Freedman, T. Zhang, Y. Jeong, and G.T.Y. Chen, Deformable model-based segmentation of 3D CT by matching distributions, in Medical Physics, vol. 31, no. 6, pp. 1711, 2004

D. Freedman, Surface reconstruction, one triangle at a time, in Proceedings of the Canadian Conference of Computational Geometry (CCCG), 2004

T. Zhang and D. Freedman, Tracking objects using density matching and shape priors, in Proceeedings of the IEEE International Conference on Computer Vision (ICCV), 2003

D. Freedman, Effective tracking through tree-search, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 604–615, 2003

Y. Shao, M. Magdon-Ismail, D. Freedman, S. Akella, and C. Bystroff, Compression of protein conformational space, in International Conference on Research in Computational Molecular Biology (RECOMB), 2002

D. Freedman, Combinatorial curve reconstruction in Hilbert Spaces: a new sampling theory and an old result revisited, in Computational Geometry: Theory and Applications, vol. 23, no. 2, pp. 227–241, 2002

D. Freedman, Efficient simplicial reconstructions of manifolds from their samples, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1349–1357, 2002

D. Freedman and M.S. Brandstein, Contour tracking in clutter: a subset approach, in International Journal of Computer Vision, vol. 38, no. 2, pp. 173–186, 2000

D. Freedman and M.S. Brandstein, Provably fast algorithms for contour tracking, in Proceeedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2000

D. Freedman, Manifold reconstruction from unorganized points, in Proceedings of the Asilomar Conference on Signals, Systems, and Computers, 2000

D. Freedman and M.S. Brandstein, A subset approach to contour tracking in clutter, in Proceeedings of the IEEE International Conference on Computer Vision (ICCV), 1999

D. Freedman and M.S. Brandstein, Methods of global optimization in the tracking of contours, in Proceedings of the Asilomar Conference on Signals, Systems, and Computers, 1999

R. Taylor, A. Sachrajda, D. Freedman, and P. Kelly, Density of electrons in a lateral quantum dot by semi-classical trajectory analysis, in Solid State Communications, vol. 89, no. 7, pp. 579–582, 1994