EDBT 2026 Demo / reviewers in the wild / expert
Dániel Szolgay
dblp:46/5892
· DBLP profile ↗
4ranked-venue papers
3as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › image filtering › nonlinear diffusion
anisotropic diffusion |
0.1 | 1 | 2012 | Adaptive Image Decomposition into Cartoon and Texture Parts Optimized by the Orthogonality Criterion · IEEE Trans. Image Process. 2012 |
Image and video processing
image decomposition |
0.1 | 1 | 2012 | Adaptive Image Decomposition into Cartoon and Texture Parts Optimized by the Orthogonality Criterion · IEEE Trans. Image Process. 2012 |
Image and video processing › image decomposition
structure-texture decomposition |
0.1 | 1 | 2012 | Adaptive Image Decomposition into Cartoon and Texture Parts Optimized by the Orthogonality Criterion · IEEE Trans. Image Process. 2012 |
Methods — techniques the papers use, named apart from their topics
total variation · 0.1parameter estimation · 0.1orthogonality criterion · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Behavioral cues help predict impact of advertising on future sales
Gábor Szirtes, Javier Orozco, István Petrás, Dániel Szolgay, Ákos Utasi, Jeffrey F. Cohn |
Image Vis. Comput. | 4 |
| 2012 | Adaptive Image Decomposition into Cartoon and Texture Parts Optimized by the Orthogonality CriterionabstractIn this paper a new decomposition method is introduced that splits the image into geometric (or cartoon) and texture parts. Following a total variation based preprocesssing, the core of the proposed method is an anisotropic diffusion with an orthogonality based parameter estimation and stopping condition. The quality criterion is defined by the theoretical assumption that the cartoon and the texture components of an image should be orthogonal to each other. The presented method has been compared to other decomposition algorithms through visual and numerical evaluation to prove its superiority. Dániel Szolgay, Tamás Szirányi |
IEEE Trans. Image Process. | 1 |
| 2011 | Detection of moving foreground objects in videos with strong camera motion
Dániel Szolgay, Jenny Benois-Pineau, Rémi Mégret, Yann Gaëstel, Jean-François Dartigues |
Pattern Anal. Appl. | 1 |
| 2010 | Orthogonality Based Stopping Condition for Iterative Image Deconvolution Methods
Dániel Szolgay, Tamás Szirányi |
ACCV (4) | 1 |