Michael Werman


The Institute of Computer Science
The Hebrew University of Jerusalem
Jerusalem 91904
Israel

e-mail: werman@cs.huji.ac.il
phone: +972-2-54-94541
fax: +972-2-54-94541
office: B506, Rothberg Building, Givat Ram Campus


Research Interests



Prospective Graduate Students / PostDocs

Selected publications on-line

Cana

Cameroon

Selected publications on-line

Efficient classification using the Euler characteristic .
 E. Richardson and M. Werman. 
Pattern Recognition Letters, 2014
Scene Geometry from Moving Objects .
 E. Richardson and S. Peleg and M. Werman. 
AVSS, 2014
Ellipses from Triangles .
 M. Cicconet and K. Gunsalus and D. Geiger and M. Werman. 
ICIP, 2014
Optical Flow for non Lambertian surfaces by cancelling illuminant chromaticity .
 C. Arora and M. Werman. 
ICIP, 2014
Shape Statistics for Cell Division Detection in Time-Lapse Videos of Early Mouse Embryo .
 M. Cicconet and K. Gunsalus and D. Geiger and M. Werman. 
ICIP, 2014
Mirror Symmetry Histograms for Capturing Geometric Properties in Images .
 M. Cicconet and D. Geiger and K. Gunsalus nd M. Werman. 
CVPR, 2014
Automatic Recovery of the Atmospheric Light in Hazy Images.
 M. Sulami and I. Geltzer and R. Fattal  and M. Werman. 
ICCP, 2013
Illuminant Chromaticity from Image Sequences.
 V. Prinet and D. Lischinski and M. Werman. 
International Conference on Computer Vision (ICCV), 2013
Specular Highlight Enhancement from Video Sequences.
 V. Prinet and M. Werman and D. Lischinski. 
ICIP, 2013
Asymmetric Correlation: a Noise Robust Similarity Measure for Template Matching.
 E. Elboher and M. Werman. 
IEEE Transactions on Image Processing (TIP), 2013
The Generalized Laplacian Distance and its Applications for Visual Matching.
E. Elboher, M. Werman, and Y. Hel-Or.
CVPR 2013.
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification.
Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman.
ICML 2013.
Efficient and Accurate Gaussian Image Filtering Using Running Sums.
E. Elboher and M. Werman.
SoCPar 2012, Brunei.
Improving Perceptual Color Difference using Basic Color Terms.
Ofir Pele and Michael Werman.
arXiv 2012.
Content-Aware Automatic Photo Enhancement.
L. Kaufman, D. Lischinski, and M. Werman.
COMPUTER GRAPHICS Forum 2012.
Extracting Scar and Ridge Features from 3D-scanned Lithic Artifacts.
E. Richardson, L. Grossman, U. Smilansky, and M. Werman.
CAA 2012.
Noniterative Exact Solution to the Phase Problem in Optical Imaging Implemented with Scanning Probe Microscope.
D. Honigstein, J. Weinroth, M. Werman, and A. Lewis.
ACS Nano, 2012, 6 (1), pp 220226. DOI: 10.1021/nn203427z
Probabilistic Approach to Pattern Matching in the Continuous Domain.
D. Keren, M. Werman, J. Feinberg.
PAMI 2012.
Cosine Integral Images for Fast Spatial and Range Filtering.
E. Elboher and M. Werman.
ICIP 2011, Brussels.
A curvelet-based patient-specific prior for accurate multi-modal brain image rigid registration.
M. Freiman,  M. Werman and L. Joskowicz.
Medical Image Analysis Volume 15, Issue 1, February 2011, Pages 125-132.
The Quadratic-Chi Histogram Distance Family.
O. Pele and M. Werman.
ECCV 2010. Code
Robust Head Pose Estimation by Fusing Time-of-Flight Depth and Color.
A. Bleiweiss and M. Werman.
MMSP 2010.
Recovering Color and Details of Clipped Image Regions.
E. Elboher and M. Werman.
CGVCVIP 2010. Project page
Robust Real Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking.
A. Bleiweiss and M. Werman.
Dynamic 3D Imaging 2009.
Fast and Robust Earth Mover's Distances.
O. Pele and M. Werman.
ICCV 2009. Code
Applying Two-Pixel Features to Face Detection.
I. Nissenboim, D. Keren, and M. Werman.
IEEE International Conference on Signal Image Technology and Internet Based Systems, 2008.
A Linear Time Histogram Metric for Improved SIFT Matchings.
O. Pele and M. Werman.
ECCV 2008. Code
Robust Real Time Pattern Matching using Bayesian Sequential Hypothesis Testing
O. Pele and M. Werman.
PAMI, 2008. Code
Accelerating Pattern Matching or How Much Can You Slide?
O. Pele and M. Werman.
ACCV, 2007. Code
Vertical Parallax from Moving Shadows.
Y. Caspi and M. Werman.
CVPR, 2006.
The Bottleneck Geodesic: Computing Pixel Affinity.
I. Omer and M. Werman.
CVPR, 2006.
Affine Invariance Revisited.
E. Begelfor and M. Werman.
CVPR, 2006.
Image Specific Feature Similarities.
I. Omer and M. Werman.
ECCV, 2006.
The World is not (always) Flat or Learning Curved Manifolds.
E. Begelfor and M. Werman.
HUJI-CSE-LTR-2006-191 PAMI, 2006.
How to Put Probabilities on Homographies.
E. Begelfor and M. Werman.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 27, NO. 10, OCTOBER 2005.
On using priors in affine matching.
V. Govindu and M. Werman.
Image and Vision Computing, V 22, 14, Dec 2004, Pages 1157-1164.
Using Natural Image Properties as Demosaicing Hints.
I. Omer and M. Werman.
ICCV 2004.
Color Lines: Image Specific Color Representation.
I. Omer and M. Werman.
CVPR 2004.
Simulation of Rain in Videos
S. Starik and  M. Werman
Texture03.
  • rain videos
  • Unsupervised Clustering of Images using their Joint Segmentation
    Y. Seldin, S. Starik  and  M. Werman
    SCTV03.

    The Viewing Graph
    N. Levi and  M. Werman
    CVPR 2003, II:599-606.

    Study of Mutual Information in Perceptual Coding with Application for Low Bit-Rate Compression.
    A. Ben-Shalom, S. Dubnov and M. Werman 
    Fourth International Symposium on Independent Component Analysis and Blind Source Separation. ICA 2003
    Improved Low bit-rate audio compression using reduced rank ICA instead of psychoacoustic modeling.
         A. Ben-Shalom, S. Dubnov and M. Werman 
    IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP2003
    Fast Convolution
         M. Werman.
    WSCG 2003, Feb 2003.

    On using Priors in Affine Matching
         V. Govindu, and M. Werman.
    Indian Conference on Computer Vision, Graphics and Image Processing, 2002.


    Gradient domain high dynamic range compression
         R. Fattal, D. Lischinski, and M. Werman.
    ACM Transactions on Graphics (Proc. ACM SIGGRAPH 2002), July 2002.

    OAll Points Considered: A Maximum Likelihood Method for Motion Recovery
         Daniel Keren and Ilan Shimshoni and  Liran Goshen and  Michael Werman.
    Theoretical Foundations of Computer Vision, Springer LNCS series 2616, 72-85, (2003).


    Parameter Estimates for a Pencil of Lines: Bounds and Estimators
         G. Speyer and M. Werman
    ECCV, 2002.

    A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
         M. Werman and D. Keren
    PAMI, 23, 5, 528-534, 2001.

    Texture mixing and texture movie synthesis using statistical learning
         Z. Bar-Joseph, R. El-Yaniv, D. Lischinski, and  M. Werman
    IEEE Transactions on Visualization and Computer Graphics, 7(2), 2001, pp. 120-135

    Self Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization
         Y. Gdalyahu, D. Weinshall and M. Werman
    IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(10):1053-1074, 2001.

    Structure from Motion using Points, Lines, and Intensities
         J. Oliensis and  M. Werman
    CVPR 2000, II:599-606.

    Model Based Pose Estimator using Linear Programming
         M. Ben-Ezra, S. Peleg and M. Werman
    ECCV2000
    Real-Time Motion Analysis with Linear Programming,
         M. Ben-Ezra, S. Peleg, and M. Werman
    Computer Vision and Image Understanding, Vol. 78, No. 1, Apr 2000, pp. 32-52.

    A Full Bayesian Approach to Curve and Surface Reconstruction
         D. Keren and M. Werman
    JMIV, 11, 27-43, 1999.

    Robot Localization using Uncalibrated Camera Invariants
         M. Werman, S. Banerjee, S. Dutta Roy and M. Qiu. 
    IEEE CVPR'99, II:353-359, June 23-25, Fort Collins, Colorado, 1999.

    Trajectory Triangulation over Conic Sections
         A. Shashua, S. Avidan and M. Werman
    International Conference on Computer Vision (ICCV) 330-336, Sep., 1999.

    Minimal Decomposition of Model-Based Invariants,

      Weinshall and M. Werman
    JMIV 10(1):77-87, 1999.

    A method for on-line clustering of non-stationary data
         I.D. Guedalia, M. London and M Werman
    Neural Computation 11:551-571, 1999.
    Real-Time Object Tracking from a Moving Video Camera: A Software Approach on a PC
         Y. Rosenberg and M. Werman
    IEEE Workshop on Applications of Comuter Vision, Princeton, Oct 1998, pp. 238-239.

    Representing local motion as a probability distribution matrix applied to object tracking
         Y. Rosenberg and M. Werman
    CVPR, 1997, pp. 654--659.

    A General Filter for Measurements with any Probability Distribution
         Y. Rosenberg and M. Werman
    CVPR, 1997, pp. 106--111.

    On View Likelihood and Stability
         D. Weinshall and M. Werman,
    IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(2):97-108, 1997.

    Duality Of Multi-Point And Multi-Frame Geometry: Fundamental Shape Matrices And Tensors
         D. Weinshall, M. Werman and A. Shashua,
    ECCV-96, II:217-227, Cambridge, April 1996.

    Similarity and Affine Invariant Distance Between Point Sets
         M. Werman and D. Weinshall
    PAMI, 17(8), pp. 810-814, August 1995.
    The Study of 3D-from-2D using Elimination
         M. Werman and A. Shashua
    Int. Conf. Computer Vision, Boston, June 1995, 473-479.
    Fitting a Second Degree Curve if Both Coordinates are Subject to Error
         M. Werman and Z. Geyzel
    PAMI, 17,207--211, 1995.
    Trilinearity of Three Perspective Views and its Associated Tensor
         A. Shashua and M. Werman
    Int. Conf. Computer Vision, Boston, June 1995, 920-925.
    Linear Time Euclidean Distance Transform and Voronoi Diagram Algorithms
         H. Breu and J. Gil and D. Kirkpatrck and M. Werman
    PAMI, 17, 529--533, 1995.
    Pose Estimation by Fusing Noisy Data of Different Dimensions
         Y. Hel-Or and M. Werman
    PAMI Vol 17, No. 2, Feb 1995.
    Localization of Primitives Using Adaptive Projections
         Y. Hel-Or and M. Werman
    Journal of Intelligent and Robotic Systems Vol 11, 161-174 1994.
    Highlight and Reflection Independent Multiresolution Textures from Image Sequences
         E. Ofek, E. Shilat, A. Rappoport, and M. Werman
    IEEE Computer Graphics and Applications, 1994, 17, 18-29.
    Model Based Pose Estimation of Articulated and Constrained Objects
         Y. Hel-Or, M. Werman
    ECCV-94, Stockholm, 267-273, May 1994.
    Stability and Likelihood of Views of Three Dimensional Objects
         D. Weinshall, M. Werman and N. Tishby
    ECCV, Stockholm, May 1994, pp. 24--35.
    Constraint-Fusion for Interpretation of Articulated Objects
         Y. Hel-Or, M. Werman
    CVPR-94, Seattle, June 1994, pp. 39--45.
    Similarity and Affine Distance Between Point Sets
         M. Werman, D. Weinshall
    12-ICPR, Jerusalem, Vol I, pp. 723-725, October 1994.
    Matching Points into Pairwise Disjoint Noise Regions: Combinatorial Bounds and Algorithms Subject to Error
         E. M. Arkin and K. Kedem and J. S. B. Mitchell and J. Sprinzak and M. Werman
    ORSA Journal on Computing, special issue on computational geometry, 27-52, 1992.
    Probabilistic Analysis of Regularization
         D. Keren and M. Werman
    PAMI, 15, 983-1001, 1993.

    Computing 2D Min, Max and Median Filters
         Y. Gil and  M. Werman
    PAMI, 15, 504-507, 1993.
    Finding the Repeated Median Regression Line
         A. Stein and M. Werman
    3'rd Symposium on Discrete Algorithms, 409--413, 1992.
    Robust Statistics in Shape Fitting
         A. Stein and M. Werman
    Computer Vision and Pattern Recognition, 540-546, 1992.
    Segmenting and Compressing by Minimal Length Encoding
         D. Keren and R. Marcus and M. Werman
    10'th International Conference on Pattern Recognition 681--683, 1990.
    A Unified Approach to the Change of Resolution: Space and Gray-Level
         S. Peleg and M. Werman and H. Rom
    PAMI 11, 739-742
    Inverting the autocorrelation and the problem of locating points on a line, given unlabelled distances between them
         P. Lemke and M. Werman
    Tech Report, 1988.
    Halftoning as Optimal Quantization
        S. Peleg and M. Werman
    8th ICPR, 1986, pp. 1114-1116.
    Bipartite graph matching for points on a line or a circle
      M. Werman, S. Peleg, R. Melter, and T.Y. Kong
    Journal of Algorithms, Vol. 7, 1986, pp. 277-284.
    A distance metric for multidimensional histograms
       M. Werman, S. Peleg, and A. Rosenfeld
    CVGIP, Vol. 32, Dec. 1985, pp. 328-336.
    Min max operators in texture analysis
       M. Werman and S. Peleg
    Trans. on PAMI, Vol 7, Nov. 1985, pp. 730-733.

    I am always looking to supervise bright and motivated graduate students in areas relating to computer vision, image processing, graphics and geometric or statistical algorithms. Please take at least a cursory look at some of my online papers before contacting me.