EDBT 2026 Demo / reviewers in the wild / expert
Miguel Segui Prieto
dblp:51/3165
· DBLP profile ↗
4ranked-venue papers
3as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorApplied, interdisciplinary, general and emerging computing · 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 · 56% Multimedia analysis and retrieval · 44% |
Topics — the 3 heaviest of 3, 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 | 2003 | A Similarity Metric for Edge Images · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Image and video processing
image matching |
0.0 | 1 | 2003 | A Similarity Metric for Edge Images · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Image and video processing
edge detection |
0.0 | 1 | 2003 | A Similarity Metric for Edge Images · IEEE Trans. Pattern Anal. Mach. Intell. 2003 |
Methods — techniques the papers use, named apart from their topics
optimal pixel matching · 0.0edge strength weighting · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Using self-organising maps in the detection and recognition of road signs
Miguel Segui Prieto, Alastair R. Allen |
Image Vis. Comput. | 1 |
| 2009 | A hybrid system for embedded machine vision using FPGAs and neural networks
Miguel Segui Prieto, Alastair R. Allen |
Mach. Vis. Appl. | 1 |
| 2003 | A Similarity Metric for Edge ImagesabstractThe performance of several discrepancy measures for the comparison of edge images is analyzed and a novel similarity metric aimed at overcoming their problems is proposed. The algorithm finds an optimal matching of the pixels between the images and estimates the error produced by this matching. The resulting Pixel Correspondence Metric (PCM) can take into account edge strength as well as the displacement of edge pixel positions in the estimation of similarity. A series of experimental tests shows the new metric to be a robust and effective tool in the comparison of edge images when a small localization error of the detected edges is allowed. Miguel Segui Prieto, Alastair R. Allen |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |
| 2000 | Interpretation of the Richardson Plot in Time Series Representation
William Blackburn, Miguel Segui Prieto, Alfons Schuster |
IDEAL | 2 |