EDBT 2026 Demo / reviewers in the wild / expert
M. Demi
dblp:43/4168
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2005
0000-0002-1472-7863ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 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.
| Computer graphics and multimedia
1 paper |
Image and video processing · 67% Visualization and visual analytics · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
hierarchical aggregation |
0.0 | 1 | 1993 | An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993 |
Image and video processing
image segmentation |
0.0 | 1 | 1993 | An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993 |
Image and video processing › image segmentation
medical image segmentation |
0.0 | 1 | 1993 | An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993 |
Methods — techniques the papers use, named apart from their topics
signal detection theory · 0.0bottom-up grouping · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | On the gray-level central and absolute central moments and the mass center of the gray-level variability in low-level image processing
M. Demi |
Comput. Vis. Image Underst. | 1 |
| 1993 | An Artificial Vision System for X-ray Images of Human Coronary TreesabstractThe coronary tree expert (CORTEX) analyzer, which is a vision system for the description of the bidimensional shape and position of coronary vessels using standard nonsubtracted radiographic images, is described. A bottom-up approach was used to deal with the typical characteristics of medical images, such as structural and nonstructural noise and complexity and variability of biological shapes. On these grounds, grouping criteria were utilized to produce intermediate image representations with an increasing complexity in a hierarchical manner (from edge points to curves, segments, bars, and finally to vessels and their mutual relations). In this way, uncertain, inconsistent, and deficient information was efficiently processed. The evaluation of CORTEX segmentation is also performed according to a signal-detection-theory-like approach.> Giuseppe Coppini, M. Demi, Riccardo Poli, Guido Valli |
IEEE Trans. Pattern Anal. Mach. Intell. | 2 |