Demo Data from VLDB 2026 / reviewers in the wild / expert
Matteo Campana
dblp:73/6752
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
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 |
Image and video processing · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › image segmentation › object segmentation
cell segmentation |
0.1 | 1 | 2010 | Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis · IEEE Trans. Image Process. 2010 |
Image and video processing › biomedical image analysis
medical image analysis |
0.1 | 1 | 2010 | Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis · IEEE Trans. Image Process. 2010 |
Methods — techniques the papers use, named apart from their topics
partial differential equations · 0.1edge-preserving filtering · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Cells Segmentation From 3-D Confocal Images of Early Zebrafish EmbryogenesisabstractWe designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective. Cecilia Zanella, Matteo Campana, Barbara Rizzi, Camilo Melani, Gonzalo Sanguinetti, Paul Bourgine, Karol Mikula, Nadine Peyriéras, Alessandro Sarti |
IEEE Trans. Image Process. | 2 |