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Michael Werman מיכאל ורמן

School of Computer Science
The Hebrew University of Jerusalem
Jerusalem 91904

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




Selected publications on-line

A Convolutional Approach to Reflection Symmetry,

Cicconet, Marcelo and Birodkar, Vighnesh and Lund, Mads and Werman, Michael and Geiger, Davi, Pattern Recognition Letters, 2017. 

Epipolar Geometry Based On Line Similarity,

G. Ben-Artzi, T. Halperin, M. Werman, and S. Peleg, ICPR'16, 2016. 

Fundamental Matrices from Moving Objects Using Line Motion Barcodes,

Y. Kasten, G. Ben-Artzi, S. Peleg, and M. Werman, ECCV'16, 2016.

Camera Calibration from Dynamic Silhouettes Using Motion Barcodes,

G. Ben-Artzi, Y. Kasten, S. Peleg, and M. Werman, CVPR'16, 2016. 

Complex-Valued Hough Transforms for Circles.

Marcelo Cicconet, Davi Geiger and Michael Werman. ICIP, 2015. 

Event Retrieval Using Motion Barcodes.

G. Ben-Artzi, M. Werman and S. Peleg. ICIP, 2015. 

Extracting Scar and Ridge Features from 3D-scanned Lithic Artifacts .

E. Richardson, L. Grosman, U. Smilansky and M. Werman. Archaeology in the Digital Era, p. 83, 2014. 

Efficient classification using the Euler characteristic.

E. Richardson and M. Werman. Pattern Recognition Letters, 2014. Code 

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.


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.

On All 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, 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

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

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

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.