Demo Data from EDBT 2026 / reviewers in the wild / expert
Andrew Ackah-Miezan
dblp:99/10792
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1993
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, 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 · 87% Multimedia analysis and retrieval · 13% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 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 segmentation
energy minimization segmentation |
0.0 | 1 | 1993 | Discrete models for energy-minimizing segmentation · ICCV 1993 |
Image and video processing
image segmentation |
0.0 | 1 | 1993 | Discrete models for energy-minimizing segmentation · ICCV 1993 |
Mathematical optimization › variational methods
energy functional optimization |
0.0 | 1 | 1993 | Discrete models for energy-minimizing segmentation · ICCV 1993 |
Multimedia analysis and retrieval › image analysis
multiresolution image analysis |
0.0 | 1 | 1993 | Discrete models for energy-minimizing segmentation · ICCV 1993 |
Methods — techniques the papers use, named apart from their topics
mumford-shah functional · 0.0deterministic multiresolution algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | Discrete models for energy-minimizing segmentationabstractThe image segmentation problem may be considered as the search for a way to subdivide an image domain into regions which represent the projection of visible parts of objects in a real scene. The authors analyze the problem of image segmentation in the framework of the approximation theory as defined by D. Mumford and J. Shah (1988). They show that for real images the problem of the choice of the energy functional is dictated by the model of the world, and they propose a method to optimize it based on a deterministic algorithm processed at multiple levels of resolution. Problems encountered in dealing with real scenes lead to several modifications of the algorithm and the energy functional. Images are shown on which the algorithm was tested.> Andrew Ackah-Miezan, André Gagalowicz |
ICCV | 1 |