Demo Data from VLDB 2026 / reviewers in the wild / expert
Axel Korn
dblp:38/4910
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 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 · 75% Multimedia analysis and retrieval · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing
edge detection |
0.0 | 1 | 1988 | Toward a Symbolic Representation of Intensity Changes in Images · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Image and video processing › edge detection
gradient-based edge detection |
0.0 | 1 | 1988 | Toward a Symbolic Representation of Intensity Changes in Images · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Multimedia analysis and retrieval
image analysis |
0.0 | 1 | 1988 | Toward a Symbolic Representation of Intensity Changes in Images · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Image and video processing › multiscale analysis › multiresolution analysis
scale selection |
0.0 | 1 | 1988 | Toward a Symbolic Representation of Intensity Changes in Images · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Methods — techniques the papers use, named apart from their topics
gradient filter bank · 0.0convolution kernel selection · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Robust grouping of intensity changes in outdoor scenes by a histogram and graph based methodabstractS.337-341 : Abb.,Lit. Axel Korn |
BMVC | 1 |
| 1988 | Toward a Symbolic Representation of Intensity Changes in ImagesabstractThe symbolic representation of gray-value variations is studied, with emphasis on the gradient of the image function. The goal is to relate the results of this analysis to the structure of the picture, which is determined by the physics of the image generation process. Candidates for contour points are the maximal magnitudes of the gray-value gradient for different scales in the direction of the gradient. Based on the output of such a bank of gradient filters, a procedure is proposed to select automatically a suitable scale, and with that, the size of the right convolution kernel. The application of poorly adapted filters, which make the exact localization of gray-value corners or T-, X-, and Y-junctions more difficult, is thus avoided. Possible gaps at such junctions are discussed for images of real scenes, and possibilities for the closure of some of these gaps are demonstrated when the extrema of the magnitudes of the gray-value gradients are used.> Axel Korn |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |