Demo Data from VLDB 2026 / reviewers in the wild / expert
P. Siy
dblp:328/6106
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1988
—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 |
Image and video processing · 100% | |
| Artificial intelligence
1 paper |
Learning theory · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing
image segmentation |
0.0 | 1 | 1988 | Thresholding based on learning theory · Proc. IEEE 1988 |
Image and video processing
real-time image processing |
0.0 | 1 | 1988 | Thresholding based on learning theory · Proc. IEEE 1988 |
Image and video processing › image segmentation
thresholding |
0.0 | 1 | 1988 | Thresholding based on learning theory · Proc. IEEE 1988 |
Methods — techniques the papers use, named apart from their topics
learning theory · 0.0iterative thresholding · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | Thresholding based on learning theoryabstractLearning theory is applied to real-time thresholding of gray-level images on a line-by-line basis. With this approach, each scan line becomes an isolated entity and a dynamic threshold value for each pixel is iteratively calculated while scanning. The approach works with horizontal or vertical scanning. The locations of objects in the image may also be identified while thresholding. This approach can be used to develop fast real-time algorithms suitable for hardware implementation.> M. H. Hassan, P. Siy |
Proc. IEEE | 2 |