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Computer Science

D-Index
37
Citations
7061
World Ranking
10607
National Ranking
171

Overview

Michael Lindenbaum is affiliated with the Technion - Israel Institute of Technology in Israel. Their research primarily spans the fields of Earth and Planetary Sciences as well as Computer Science, with a significant focus on Oceanography, Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, and Geometry and Topology as notable subfields of study.

Their work addresses a variety of topics including Underwater Acoustics Research, Ocean Waves and Remote Sensing, Oceanographic and Atmospheric Processes, Medical Image Segmentation Techniques, Visual Attention and Saliency Detection, Advanced Neural Network Applications, and Adversarial Robustness in Machine Learning.

Michael Lindenbaum has contributed to several papers across reputable venues:

  • Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images, 2023, Measurement Science and Technology
  • Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array, 2025, Data in Brief
  • Assessing Hierarchies by Their Consistent Segmentations, 2024, Journal of Mathematical Imaging and Vision
  • Wave (from) Polarized Light Learning (Wpll) Method: High Resolution Spatio-Temporal Measurements of Water Surface Waves in Laboratory Setups, 2024, SSRN Electronic Journal
  • From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task, 2023, arXiv (Cornell University)

Frequent publication venues for Michael Lindenbaum include:

  • arXiv (Cornell University)
  • Measurement Science and Technology
  • Data in Brief
  • Journal of Mathematical Imaging and Vision
  • SSRN Electronic Journal

Collaboration plays a notable role in their research, with frequent co-authors being Noam Ginio, Dan Liberzon, Barak Fishbain, Thomas Dagès, and Alfred M. Bruckstein. These collaborations indicate a strong engagement with peers in both experimental and computational aspects of their fields.

Best Publications

  • The farthest point strategy for progressive image sampling

    Y. Eldar;M. Lindenbaum;M. Porat;Y.Y. Zeevi

  • Sequential Karhunen-Loeve basis extraction and its application to images

    A. Levey;M. Lindenbaum

  • Sequential Karhunen-Loeve basis extraction and its application to images

    A. Levy;M. Lindenbaum

  • Distributed covering by ant-robots using evaporating traces

    I.A. Wagner;M. Lindenbaum;A.M. Bruckstein

  • On Gabor's contribution to image enhancement

    Michael Lindenbaum;M. Fischer;Alfred M. Bruckstein

  • Selective Sampling for Nearest Neighbor Classifiers

    Michael Lindenbaum;Shaul Markovitch;Dmitry Rusakov

  • On the metric properties of discrete space-filling curves

    C. Gotsman;M. Lindenbaum

  • 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks

    Yizhak Ben-Shabat;Michael Lindenbaum;Anath Fischer

  • Mean Shift tracking with multiple reference color histograms

    Ido Leichter;Michael Lindenbaum;Ehud Rivlin

  • Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling

    T. Avraham;M. Lindenbaum

  • Geometric voting algorithm for star trackers

    M. Kolomenkin;S. Pollak;I. Shimshoni;M. Lindenbaum

  • Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis

    R Sandler;M Lindenbaum

  • Learning implicit transfer for person re-identification

    Tamar Avraham;Ilya Gurvich;Michael Lindenbaum;Shaul Markovitch

  • A generic grouping algorithm and its quantitative analysis

    A. Amir;M. Lindenbaum

  • Efficiently searching a graph by a smell-oriented vertex process

    Israel A. Wagner;Michael Lindenbaum;Alfred M. Bruckstein

  • Shape Reconstruction of 3D Bilaterally Symmetric Surfaces

    Ilan Shimshoni;Yael Moses;Michael Lindenbaum

  • Learning distributions by their density levels: a paradigm for learning without a teacher

    Shai Ben-David;Michael Lindenbaum

  • Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds Using Convolutional Neural Networks

    Yizhak Ben-Shabat;Michael Lindenbaum;Anath Fischer

  • MAC Versus PC: Determinism and Randomness as Complementary Approaches to Robotic Exploration of Continuous Unknown Domains

    Israel A. Wagner;Michael Lindenbaum;Alfred M. Bruckstein

  • Tracking by Affine Kernel Transformations Using Color and Boundary Cues

    I. Leichter;M. Lindenbaum;E. Rivlin

  • 2009 IEEE 12th International Conference on Computer Vision (ICCV)

    Stephen Gould;Richard Fulton;Daphne Koller;Ido Leichter

Frequent Co-Authors

Alfred M. Bruckstein
Alfred M. Bruckstein Technion – Israel Institute of Technology
Shaul Markovitch
Shaul Markovitch Technion – Israel Institute of Technology
Ehud Rivlin
Ehud Rivlin Technion – Israel Institute of Technology
Shai Ben-David
Shai Ben-David University of Waterloo
Arnon Amir
Arnon Amir IBM (United States)
Yehoshua Y. Zeevi
Yehoshua Y. Zeevi Technion – Israel Institute of Technology
David W. Jacobs
David W. Jacobs University of Maryland, College Park
Craig Gotsman
Craig Gotsman New Jersey Institute of Technology
Nahum Kiryati
Nahum Kiryati Tel Aviv University
Arnold W. M. Smeulders
Arnold W. M. Smeulders University of Amsterdam

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