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Josef Maier

dblp:24/994 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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.

Artificial intelligence
1 paper
3D vision · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › motion estimation
optical flow		 0.212016
Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis · ECCV (7) 2016
Bioinformatics and computational biology › genome annotation
functional region identification		 0.212013
SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013
Bioinformatics and computational biology
protein sequence analysis		 0.212013
SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013
Bioinformatics and computational biology › sequence analysis › motif discovery
protein motif discovery		 0.012013
SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013

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

scaled profiles · 0.2decoy sequences · 0.2amino acid composition · 0.2
YearPublicationVenuePosition
2016 Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis
Josef Maier, Martin Humenberger, Markus Murschitz, Oliver Zendel 0001, Markus Vincze
 ECCV (7)1
2013 SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules
abstract
SUMMARY: Functional modules within protein sequences are often extracted by consensus sequence patterns representing a linear motif; however, other functional regions may only be described by combined features such as amino acid composition, profiles of amino acid properties and randomly distributed short sequence motifs. If only a small number of functional examples are well characterized, the researcher needs a tool to extract similar sequences for further investigation. AVAILABILITY AND IMPLEMENTATION: We provide the web application 'SAPA tool', which allows the user to search with combined properties, ranks the extracted target regions by an integrated score, estimates false discovery rates by using decoy sequences and provides them as a sequence file or spreadsheet. Source code, user manual and the web application implemented in Perl, HTML, CSS and JavaScript and running on Apache are freely available at http://sapa-tool.uio.no/sapa/
Josef Maier, Alexei A. Adzhubei, Wolfgang Egge-Jacobsen
 Bioinform.1