Demo Data from EDBT 2026 / reviewers in the wild / expert
Stevan R. Stark Jr.
dblp:139/4692
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Geometric modeling and processing · 100% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing
feature recognition |
0.0 | 1 | 1999 | Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999 |
Geometric modeling and processing › feature recognition
machining feature recognition |
0.0 | 1 | 1999 | Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999 |
Geometric modeling and processing
reverse engineering |
0.0 | 1 | 1999 | Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999 |
Computer vision › 3D vision
3d reconstruction |
0.0 | 1 | 1999 | Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999 |
Computer vision › 3D vision › 3d reconstruction › object reconstruction
CAD model reconstruction |
0.0 | 1 | 1999 | Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999 |
Methods — techniques the papers use, named apart from their topics
feature-based modeling · 0.03d digitizer · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Feature-based reverse engineering of mechanical partsabstractReverse engineering of mechanical parts requires extraction of information about an instance of a particular part sufficient to replicate the part using appropriate manufacturing techniques. This is important in a wide variety of situations, since functional CAD models are often unavailable or unusable for parts which must be duplicated or modified. Computer vision techniques applied to three-dimensional (3-D) data acquired using noncontact, 3-D position digitizers have the potential for significantly aiding the process. Serious challenges must be overcome, however, if sufficient accuracy is to be obtained and if models produced from sensed data are to be truly useful for manufacturing operations. The paper describes a prototype of a reverse engineering system which uses manufacturing features as geometric primitives. This approach has two advantages over current practice. The resulting models can be directly imported into feature-based CAD systems without loss of the semantics and topological information inherent in feature-based representations. In addition, the feature-based approach facilitates methods capable of producing highly accurate models, even when the original 3-D sensor data has substantial errors. William B. Thompson, Jonathan C. Owen, H. James de St. Germain, Stevan R. Stark Jr., Thomas C. Henderson |
IEEE Trans. Robotics Autom. | 4 |