Demo Data from EDBT 2026 / reviewers in the wild / expert
J. Ross Stenstrom
dblp:26/2432
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 1992
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 4 first-authorSystems, architecture and hardware · 3 · 3 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.
| Artificial intelligence
4 papers |
3D vision · 74% Image recognition and object detection · 16% Representation and self-supervised learning · 10% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d reconstruction |
0.0 | 2 | 1992 | Constructing object models from multiple images · Int. J. Comput. Vis. 1992 Building wire frames from multiple range views · ICRA 1986 |
Computer vision › 3D vision › object modeling
geometric modeling |
0.0 | 3 | 1986 | Building wire frames from multiple range views · ICRA 1986 Finding points of high curvature and critical curves from a volume model · ICRA 1985 Syntactic pattern recognition for robot vision · ICRA 1984 |
Machine learning › Representation and self-supervised learning › representation learning
feature extraction |
0.0 | 1 | 1985 | Finding points of high curvature and critical curves from a volume model · ICRA 1985 |
Computer vision › 3D vision › 3d shape analysis › geometric attribute estimation
surface curvature estimation |
0.0 | 1 | 1985 | Finding points of high curvature and critical curves from a volume model · ICRA 1985 |
Computer vision › Image recognition and object detection
object recognition |
0.0 | 1 | 1984 | Syntactic pattern recognition for robot vision · ICRA 1984 |
Computer vision › Image recognition and object detection › structural pattern recognition
syntactic pattern recognition |
0.0 | 1 | 1984 | Syntactic pattern recognition for robot vision · ICRA 1984 |
Methods — techniques the papers use, named apart from their topics
range view integration · 0.0line segment extraction · 0.0volume representation · 0.0graph matching · 0.0curve partitioning · 0.0curvature operators · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1992 | Constructing object models from multiple images
J. Ross Stenstrom, C. Ian Connolly |
Int. J. Comput. Vis. | 1 |
| 1986 | Building wire frames from multiple range viewsabstractThis paper describes a process by which wire frames of polyhedra may be generated from multiple range views of a scene. Line segments are extracted from each view and transformed into a world coordinate system to produce the wire frames. These wire frames are used in the construction of full polyhedral models with the algorithm of Markowsky and Wesley. J. Ross Stenstrom, C. Ian Connolly |
ICRA | 1 |
| 1985 | Finding points of high curvature and critical curves from a volume modelabstractThis paper presents a method to evaluate surface curvature at points on the three-dimensional shell of an object using a volume representation. By considering points where the curvature is significantly high to be feature points, curves may be constructed on the surface of the object model. Such descriptive curves will become useful in finding orientation or making identification in robotics applications. Explicit formulation and experimental results of curvature operators and a corner-finding operator are presented. J. Ross Stenstrom |
ICRA | 1 |
| 1984 | Syntactic pattern recognition for robot visionabstractVision will be increasingly important in robotics applications. An object is modeled by critical curves extracted from the object. In the simplest case the object's curve is its outline. A solution to the curve partitioning problem is shown for nonconvex objects. Curves are described in a rotation and translation invariant way. A way to build a combined model database of many classes of objects is presented. A test to insure disjointness of model classes is given. An efficient technique for computing a network of these curves from a gray-level frame is presented. A graph algorithm is presented to match the model in an efficient way, independent of the scaling found in the scene. A technique for computing and classifying more general critical curves from three-dimensional data is developed. J. Ross Stenstrom |
ICRA | 1 |