Demo Data from EDBT 2026 / reviewers in the wild / expert
Brian P. Jackson
dblp:07/8116
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › image registration
aerial video registration |
0.1 | 1 | 2010 | Registering Aerial Video Images Using the Projective Constraint · IEEE Trans. Image Process. 2010 |
Image and video processing
image registration |
0.1 | 1 | 2010 | Registering Aerial Video Images Using the Projective Constraint · IEEE Trans. Image Process. 2010 |
Image and video processing › motion analysis
motion detection |
0.1 | 1 | 2010 | Registering Aerial Video Images Using the Projective Constraint · IEEE Trans. Image Process. 2010 |
Image and video processing › motion analysis
moving object detection |
0.1 | 1 | 2010 | Registering Aerial Video Images Using the Projective Constraint · IEEE Trans. Image Process. 2010 |
Methods — techniques the papers use, named apart from their topics
projective constraints · 0.1feature point matching · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Registering Aerial Video Images Using the Projective ConstraintabstractTo separate object motion from camera motion in an aerial video, consecutive frames are registered at their planar background. Feature points are selected in consecutive frames and those that belong to the background are identified using the projective constraint. Corresponding background feature points are then used to register and align the frames. By aligning video frames at the background and knowing that objects move against the background, a means to detect and track moving objects is provided. Only scenes with planar background are considered in this study. Experimental results show improvement in registration accuracy when using the projective constraint to determine the registration parameters as opposed to finding the registration parameters without the projective constraint. Brian P. Jackson, A. Ardeshir Goshtasby |
IEEE Trans. Image Process. | 1 |