World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
10362
World Ranking
8231
National Ranking
138

Overview

Michael Werman is affiliated with the Hebrew University of Jerusalem in Israel. Their academic work centers on computer science and engineering, with notable activity in subfields including computer vision and pattern recognition, artificial intelligence, computational mechanics, aerospace engineering, and computer graphics and computer-aided design.

The scientist's research touches on multiple topics such as advanced vision and imaging, robotics and sensor-based localization, 3D shape modeling and analysis, optical measurement and interference techniques, image enhancement techniques, computer graphics and visualization techniques, and computational geometry and mesh generation.

Their recent publications include the following papers:

  • MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction, 2021, IEEE Transactions on Intelligent Transportation Systems
  • An Approach to Robust ICP Initialization, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Robust affine point matching via quadratic assignment on Grassmannians, 2024, Pattern Recognition Letters
  • Approximating a Function with a Jump Discontinuity-The High-Noise Case, 2024, AppliedMath
  • Using a Supervised Method without supervision for foreground segmentation, 2020, arXiv (Cornell University)

Michael Werman frequently collaborates with a number of coauthors, including:

  • Steven B. Damelin
  • Alexander Kolpakov
  • David L. Ragozin
  • Noam Gottlieb
  • Danail Brezov

The venues in which they commonly publish reflect their research interests and include:

  • arXiv (Cornell University)
  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Pattern Recognition Letters
  • AppliedMath

Michael Werman's body of work demonstrates a sustained engagement with theoretical and applied aspects of computer science and engineering. Their publications reveal a focus on computational techniques for image analysis, geometric modeling, and localization processes that underpin applications in fields such as robotics and transportation systems.

Best Publications

  • Gradient domain high dynamic range compression

    Raanan Fattal;Dani Lischinski;Michael Werman

  • Fast and robust Earth Mover's Distances

    Ofir Pele;Michael Werman

  • Linear time Euclidean distance transform algorithms

    H. Breu;J. Gil;D. Kirkpatrick;M. Werman

  • A Linear Time Histogram Metric for Improved SIFT Matching

    Ofir Pele;Michael Werman

  • Texture mixing and texture movie synthesis using statistical learning

    Z. Bar-Joseph;R. El-Yaniv;D. Lischinski;M. Werman

  • Color lines: image specific color representation

    I. Omer;M. Werman

  • The quadratic-chi histogram distance family

    Ofir Pele;Michael Werman

  • Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization

    Y. Gdalyahu;D. Weinshall;M. Werman

  • A distance metric for multidimensional histograms

    Michael Werman;Shmuel Peleg;Azriel Rosenfeld

  • A unified approach to the change of resolution: space and gray-level

    S. Peleg;M. Werman;H. Rom

  • Computing 2-D min, median, and max filters

    J. Gil;M. Werman

  • Automatic recovery of the atmospheric light in hazy images

    Matan Sulami;Itamar Glatzer;Raanan Fattal;Mike Werman

  • Affine Invariance Revisited

    E. Begelfor;M. Werman

  • Trilinearity of three perspective views and its associated tensor

    A. Shashua;M. Werman

  • Similarity and affine invariant distances between 2D point sets

    M. Werman;D. Weinshall

  • A Bayesian method for fitting parametric and nonparametric models to noisy data

    M. Werman;D. Keren

  • On computing majority by comparisons

    Michael E. Saks;Michael E. Saks;Michael Werman

  • Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking

    Amit Bleiweiss;Michael Werman

  • Min-Max Operators in Texture Analysis

    Michael Werman;Shmuel Peleg

  • Synthesizing sound textures through wavelet tree learning

    S. Dubnov;Z. Bar-Joseph;R. El-Yaniv;D. Lischinski

Frequent Co-Authors

Shmuel Peleg
Shmuel Peleg Hebrew University of Jerusalem
Daphna Weinshall
Daphna Weinshall Hebrew University of Jerusalem
Dani Lischinski
Dani Lischinski Hebrew University of Jerusalem
Amir Shmuel
Amir Shmuel Montreal Neurological Institute and Hospital
Amnon Shashua
Amnon Shashua Hebrew University of Jerusalem
Ran El-Yaniv
Ran El-Yaniv Technion – Israel Institute of Technology
Ari Rappoport
Ari Rappoport Hebrew University of Jerusalem
Ziv Bar-Joseph
Ziv Bar-Joseph Carnegie Mellon University
Naftali Tishby
Naftali Tishby Hebrew University of Jerusalem
Joseph S. B. Mitchell
Joseph S. B. Mitchell Stony Brook University

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