Demo Data from SIGIR 2026 / reviewers in the wild / expert
David Iluz
dblp:208/4278
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Computational photography and imaging · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging › underwater imaging
underwater image formation |
0.3 | 1 | 2017 | What is the Space of Attenuation Coefficients in Underwater Computer Vision? · CVPR 2017 |
Computational photography and imaging
image formation |
0.1 | 1 | 2017 | What is the Space of Attenuation Coefficients in Underwater Computer Vision? · CVPR 2017 |
Methods — techniques the papers use, named apart from their topics
ocean optical measurement prior · 0.3manifold fitting · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | What is the Space of Attenuation Coefficients in Underwater Computer Vision?abstractUnderwater image reconstruction methods require the knowledge of wideband attenuation coefficients per color channel. Current estimation methods for these coefficients require specialized hardware or multiple images, and none of them leverage the multitude of existing ocean optical measurements as priors. Here, we aim to constrain the set of physically-feasible wideband attenuation coefficients in the ocean by utilizing water attenuation measured worldwide by oceanographers. We calculate the space of valid wideband effective attenuation coefficients in the 3D RGB domain and find that a bound manifold in 3-space sufficiently represents the variation from the clearest to murkiest waters. We validate our model using in situ experiments in two different optical water bodies, the Red Sea and the Mediterranean. Moreover, we show that contradictory to the common image formation model, the coefficients depend on the imaging range and object reflectance, and quantify the errors resulting from ignoring these dependencies. Derya Akkaynak, Tali Treibitz, Tom Shlesinger, Yossi Loya, Raz Tamir, David Iluz |
CVPR | 6 |