Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sohail Nadimi
dblp:64/2547
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author
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 |
Image and video processing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing
motion analysis |
0.0 | 1 | 2004 | Physical Models for Moving Shadow and Object Detection in Video · IEEE Trans. Pattern Anal. Mach. Intell. 2004 |
Image and video processing › image enhancement › shadow detection and removal › shadow detection
moving shadow detection |
0.0 | 1 | 2004 | Physical Models for Moving Shadow and Object Detection in Video · IEEE Trans. Pattern Anal. Mach. Intell. 2004 |
Image and video processing › motion analysis
moving object detection |
0.0 | 1 | 2004 | Physical Models for Moving Shadow and Object Detection in Video · IEEE Trans. Pattern Anal. Mach. Intell. 2004 |
Methods — techniques the papers use, named apart from their topics
spatio-temporal albedo test · 0.0dichromatic reflection model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Cooperative Coevolution Fusion for Moving Object Detection
Sohail Nadimi, Bir Bhanu |
GECCO (1) | 1 |
| 2004 | Physical Models for Moving Shadow and Object Detection in VideoabstractCurrent moving object detection systems typically detect shadows cast by the moving object as part of the moving object. In this paper, the problem of separating moving cast shadows from the moving objects in an outdoor environment is addressed. Unlike previous work, we present an approach that does not rely on any geometrical assumptions such as camera location and ground surface/object geometry. The approach is based on a new spatio-temporal albedo test and dichromatic reflection model and accounts for both the sun and the sky illuminations. Results are presented for several video sequences representing a variety of ground materials when the shadows are cast on different surface types. These results show that our approach is robust to widely different background and foreground materials, and illuminations. Sohail Nadimi, Bir Bhanu |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |