World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
9035
World Ranking
9148
National Ranking
3892

Overview

Michele Covell is affiliated with Google in the United States and specializes in computer science, with a focus on several key areas within this field. Their research predominantly covers computer vision and pattern recognition, with additional work in signal processing, artificial intelligence, control and systems engineering, and media technology.

The scientist's research topics include:

  • Video Analysis and Summarization
  • Advanced Vision and Imaging
  • Advanced Image Processing Techniques
  • Image and Signal Denoising Methods
  • Human Motion and Animation
  • Advanced Image Fusion Techniques
  • Music and Audio Processing

Michele Covell has contributed to a variety of publications in both journals and conference proceedings. Recent papers authored or coauthored by Covell include:

  • Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression, 2021, IEEE Journal of Selected Topics in Signal Processing
  • Semantically meaningful attributes from co-listen embeddings for playlist exploration and expansion, 2020, Zenodo (CERN European Organization for Nuclear Research)
  • Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression, 2021, arXiv (Cornell University)
  • Interpretable Actions: Controlling Experts with Understandable Commands, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Image Triangulation Using the Sobel Operator for Vertex Selection (Media Exposition), 2024, Dagstuhl Research Online Publication Server

Frequent coauthors collaborating with Michele Covell include:

  • Shumeet Baluja
  • David Marwood
  • A. Murat Tekalp
  • Radu Timofte
  • Chao Dong

The venues in which Covell has published reflect a range of prestigious outlets across computer science and artificial intelligence, including:

  • IEEE Journal of Selected Topics in Signal Processing
  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Dagstuhl Research Online Publication Server

Best Publications

  • Video Rewrite: driving visual speech with audio

    Christoph Bregler;Michele Covell;Malcolm Slaney

  • Full Resolution Image Compression with Recurrent Neural Networks

    George Toderici;Damien Vincent;Nick Johnston;Sung Jin Hwang

  • Three dimensional object pose estimation which employs dense depth information

    Michele M. Covell;Michael Hongmai Lin;Ali Rahimi;Michael Harville

  • Method and system for estimating jointed-figure configurations

    Michele Covell;Subutai Ahmed

  • Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

    Nick Johnston;Damien Vincent;David Minnen;Michele Covell

  • Variable Rate Image Compression with Recurrent Neural Networks

    George Toderici;Sean M. O'Malley;Sung Jin Hwang;Damien Vincent

  • Detecting repeating content in broadcast media

    Shumeet Baluja;Michele Covell;Michael Fink

  • System and method for selecting advertisements

    Malcolm Slaney;Bonnie M. Johnson;Annarosa Tomasi;Steven E. Saunders

  • Canonical correlation analysis of image/control-point location coupling for the automatic location of control points

    Michele Covell;Malcolm Slaney

  • FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks

    Malcolm Slaney;Michele Covell

  • Social and interactive applications for mass media

    Michael Fink;Shumeet Baluja;Michele Covell

  • Method for managing a streaming media service

    Michael Harville;Michele Covell;Susie J. Wee;Roy Sumit

  • Videowiedergabe mit synchronisiertem ton bei veränderlicher geschwindigkeit

    Neal A Bhadkamkar;Subutai Ahmad;Michele Covell

  • Detection and classification of matches between time-based media

    Michele Covell;Jay Yagnik;Jeff Faust;Shumeet Baluja

  • Automatic audio morphing

    M. Slaney;M. Covell;B. Lassiter

  • Communication and collaboration system using rich media environments

    Susie J. Wee;Henry Harlyn Baker;Nina T. Bhatti;Michele Covell

  • Waveprint: Efficient wavelet-based audio fingerprinting

    Shumeet Baluja;Michele Covell

  • Audio Fingerprinting: Combining Computer Vision & Data Stream Processing

    S. Baluja;M. Covell

  • Content Fingerprinting Using Wavelets

    Shumeet Baluja;Michele Covell

  • Automatic selection of a visual image or images from a collection of visual images, based on an evaluation of the quality of the visual images

    Michele M. Covell;Subutai Ahmad;Katerina L. Shiffer

Frequent Co-Authors

Shumeet Baluja
Shumeet Baluja Google (United States)
John G. Apostolopoulos
John G. Apostolopoulos Cisco Systems (United States)
George Toderici
George Toderici Google (United States)
Malcolm Slaney
Malcolm Slaney Stanford University
Rahul Sukthankar
Rahul Sukthankar Google (United States)
Henry Allan Rowley
Henry Allan Rowley Google (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing further education in computer science opens a range of career opportunities, whether you’re seeking an advanced degree or an accessible entry point. For those aiming to specialize and boost their earning potential, reviewing the best masters degree to get can help you identify programs that are both in-demand and valuable in the tech industry.

If you’re looking for flexibility or want to start your journey with foundational knowledge, online associates programs offer affordable and accessible options. These degrees can often be completed in two years and may open doors to entry-level roles or further academic pursuit.

Affordability is a key consideration for many students. Exploring the cheapest online degrees can help you find quality programs that fit your budget, making education more accessible without sacrificing academic rigor.

Finally, if your previous academic record poses a challenge, don’t let a low GPA hold you back. There are several best colleges for low gpa that provide pathways for motivated students to begin or continue their computer science education.

Best Scientists Citing Michele Covell

Trending Scientists

Recently Published Articles