PeopleofSuperintelligence

Researcher

Daniel Snow

Daniel Snow is a computer vision researcher whose work spans road-sign detection, deformable templates, pedestrian detection, and 3D reconstruction. In 1998, he coauthored Signfinder, a method for detecting and normalizing road signs; in 1999, he coauthored work on shape and albedo under varying illumination. In 2000, he coauthored research on deformable-template detection. His pedestrian paper with Paul Viola and Michael Jones won ICCV's 2003 Marr Prize. In 2008, he and Michael Jones presented a pedestrian detector using boosted features over many frames; in 2015, he was affiliated with Mitsubishi Electric Research Laboratories while coauthoring Line-Sweep for cross-ratio matching and 3D reconstruction.

Timeline

  1. 1998

    Signfinder road-sign detection

    In 1998, he coauthors Signfinder with Alan L. Yuille and Mark Nitzberg. The paper describes a method to detect, locate, and normalize road signs.

  2. 1999

    Shape and albedo under illumination

    In 1999, he coauthors a paper on shape and albedo under varying illumination, using SVD and integrability.

  3. 2000

    Deformable-template detection

    In 2000, he coauthors a paper on detecting and localizing deformable templates without user initialization. Computer Vision and Image Understanding publishes it in June 2000.

  4. 2003

    The ICCV Marr Prize

    In 2003, his pedestrian-detection paper with Paul Viola and Michael Jones wins ICCV's Marr Prize.

  5. 2008

    Pedestrian detection across frames

    At ICPR in 2008, he and Michael Jones present Pedestrian Detection Using Boosted Features Over Many Frames.

  6. 2015

    Line-Sweep at MERL

    In 2015, he is affiliated with Mitsubishi Electric Research Laboratories and coauthors Line-Sweep, which uses cross-ratio geometry for wide-baseline matching and 3D reconstruction.

  1. 23% of people shaping AI who left a university for a company later returned to a universityIncludes Mitsubishi Group. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Mitsubishi Group. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  4. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Signfinder: Using Color to Detect, Localize and Identify Informational Signs | Sciweavers sciweavers.org. Checked 21 October 2026.
  2. International Journal of Computer Vision, Volume 35 sigmod.org. Checked 21 October 2026.
  3. Computer Vision and Image Understanding, Volume 78 sigmod.org. Checked 21 October 2026.
  4. Computer Vision Misc Reading Group 2004 cs.cmu.edu. Checked 21 October 2026.
  5. Wednesday Session Index iapr.org. Checked 21 October 2026.
  6. ORBilu: Line-Sweep: Cross-Ratio for Wide-Baseline Matching and 3D Reconstruction - 2015 orbilu.uni.lu. Checked 21 October 2026.
  7. Line-sweep: Cross-ratio for wide-baseline matching and 3D reconstruction — Lacuna lacuna.tiptreesystems.com. Checked 21 October 2026.