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Sohail Nadimi

dblp:64/2547 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Image and video processing
motion analysis
0.012004
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
0.012004
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
0.012004
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
YearPublicationVenuePosition
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 Video
abstract
Current 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