Demo Data from SIGIR 2026 / reviewers in the wild / expert
Susan M. Haynes
dblp:166/9996 · also Susan Makrouhee Haynes
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › motion estimation
motion correspondence |
0.0 | 1 | 1991 | Trajectories and events · CVPR 1991 |
Methods — techniques the papers use, named apart from their topics
line fitting · 0.0constraint satisfaction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Using the atmel AVR (arduino microcontroller) as the foundation of beginning computer organization course (abstract only)abstractThis poster describes our experiences, materials and plans using the Atmel AVR, a RISC microprocessor, for teaching beginning computer organization at the college level. In particular, we use this microprocessor, typically packaged into the Arduino, to give hands-on experience with: (1) Assembly language programming, including ISA (16 bit instructions), multiple types of branches, multiple addressing modes, (2) C language programming, (3) interrupt programming, (4) I/O programming, and (5) programming on-board components, e.g., timers/counters. Susan M. Haynes |
SIGCSE | 1 |
| 1991 | Trajectories and eventsabstractA solution to the correspondence problem using constraint satisfaction is described. It uses a real-time line-fitting algorithm to detect changes in a point's motion parameters as they happen. A trajectory is hypothesized for a single point's motion. Since the event detected may be wrong, multiple trajectories are hypothesized for each point. A correspondence is drawn from the set of hypothesized trajectories. The algorithm is robust, working on noisy and non-rigid motion data, and does not use false precision.> Susan M. Haynes, Ramesh Jain 0001 |
CVPR | 1 |
| 1983 | Detection of moving edges
Susan M. Haynes, Ramesh Jain 0001 |
Comput. Vis. Graph. Image Process. | 1 |
| 1982 | Detection of moving edges
Susan M. Haynes, Ramesh Jain 0001 |
Comput. Graph. Image Process. | 1 |