Demo Data from EDBT 2026 / reviewers in the wild / expert
Nikolaos P. Nikolaou
dblp:59/6224
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
0000-0003-2753-2309ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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 |
Multimedia analysis and retrieval · 38% Image and video processing · 38% Image and video coding · 23% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › image analysis
image comparison |
0.0 | 1 | 2004 | Efficient computation of the Hutchinson metric between digitized images · IEEE Trans. Image Process. 2004 |
Image and video coding
image compression |
0.0 | 1 | 2004 | Efficient computation of the Hutchinson metric between digitized images · IEEE Trans. Image Process. 2004 |
Image and video coding
image quality assessment |
0.0 | 1 | 2004 | Efficient computation of the Hutchinson metric between digitized images · IEEE Trans. Image Process. 2004 |
Methods — techniques the papers use, named apart from their topics
hutchinson metric computation · 0.0digitized screen projection · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Efficient computation of the Hutchinson metric between digitized imagesabstractThe Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. To test the performance of our method, we apply it to compare pairs of fractal objects, as well as to compare real-world images with the corresponding reconstructed ones. Vasileios Drakopoulos, Nikolaos P. Nikolaou |
IEEE Trans. Image Process. | 2 |