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EDBT 2026 Demo / reviewers in the wild / expert

Cosmin Mihai

dblp:31/482 · DBLP profile ↗
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3ranked-venue papers
0as 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 · 2Graphics, computer vision, multimedia, augmented reality and games · 1Applied, 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 · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › multiscale analysis › multiresolution analysis
scale selection		 0.112009
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images · Int. J. Comput. Vis. 2009
Image and video processing › multiscale analysis › multiresolution analysis
scale-space analysis		 0.112009
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images · Int. J. Comput. Vis. 2009
Image and video processing
vector-valued image processing		 0.112009
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images · Int. J. Comput. Vis. 2009

Methods — techniques the papers use, named apart from their topics

scale-space representation · 0.1
YearPublicationVenuePosition
2009 Multispectral Data Classification based on Spectral Indices and Cascaded Fuzzy C-mean Classifiers
abstract
Land Use and Land Cover (LULC) are characterized by a large variety of spectrally distinct LULC classes. The diagnostic and evaluation of the spectral separability measure yields the potential for automated identification and mapping of these classes. This study proposes a new cascaded fuzzy C-mean classification method for a rough classification of (LULC) classes. The method is based on the use of spectral indices as innovative features to provide a coarse classification of remotely sensed data. The robustness and accuracy of the defined classification schema is performed based on the computation of confusion matrices and Kappa coefficient. The Kappa statistic ranges from 0.90 to 0.98 for the set of the evaluated images which infer the good accuracy of the new rough classification scheme.
Mohamed Jabloun, Cosmin Mihai, Iris Vanhamel, Thomas Geerinck, Hichem Sahli
 IGARSS (3)2
2009 Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
Iris Vanhamel, Cosmin Mihai, Hichem Sahli, Antonis Katartzis, Ioannis Pratikakis
 Int. J. Comput. Vis.2
2007 Graph Cuts Approach to MRF Based Linear Feature Extraction in Satellite Images
Anesto del-Toro-Almenares, Cosmin Mihai, Iris Vanhamel, Hichem Sahli
 CIARP2