Michael Eckmann

443 citations
12 papers · 327 · h-index 8

Impact in

Papers in

Michael Eckmann

12 papers receiving 309 citations

Peers

Michael Eckmann
Comparison fields: 5 of 67
  • Computer Vision and Pattern Recognition 235
  • Statistics, Probability and Uncertainty 33
  • Media Technology 32
  • Computer Science Applications 14
  • Artificial Intelligence 66
Replace Subhashini Ganapathy with:
Subhashini Ganapathy United States
Xiaodong Jiang China
Haw-Shiuan Chang United States
S. Rajkumar India
Pavel Král Czechia
Ricardo Sousa Portugal
Franz L. Alt United States
Shan Jia China
Yangzhou Du China
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Citations per field
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Citations per year

Countries citing papers authored by Michael Eckmann

Since Specialization
Citations

This map shows the geographic impact of Michael Eckmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Eckmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Eckmann more than expected).

Fields of papers citing papers by Michael Eckmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Eckmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Eckmann. The network helps show where Michael Eckmann may publish in the future.

Co-authors

The 10 scholars most cited alongside Michael Eckmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Eckmann Line = papers co-authored together Michael Eckmann links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2001119
2 200454
3 201344
4 201135
5 201226
6 201419
7 201316
8 20158
9
Discrete mathematics/structures: how do we deal with the late appreciation problem?
20093
10
A content-based image retrieval programming assignments for introductory computer science courses
20111
11 20011
12 20081

About Michael Eckmann

Michael Eckmann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Information Systems and Computer Science Applications, having authored 12 papers that have together received 327 indexed citations. Recurring topics across this work include Infrared Target Detection Methodologies (2 papers), Advanced Vision and Imaging (2 papers), Video Surveillance and Tracking Methods (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Digital Media Forensic Detection (2 papers), Robotics and Sensor-Based Localization (2 papers), Big Data and Business Intelligence (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (235 citations), Statistics, Probability and Uncertainty (33 citations), Media Technology (32 citations), Computer Science Applications (14 citations) and Artificial Intelligence (66 citations). Michael Eckmann has collaborated with scholars based in United States, Brazil and Germany. Frequent co-authors include Xiang Gao, T.E. Boult, Ross J. Micheals, Anderson Rocha, Walter J. Scheirer, Jacques Wainer, Terrance E. Boult, Michael J. Wilber, Siome Goldenstein and David Gries. Their work appears in journals such as Pattern Recognition Letters, Image and Vision Computing, Scientometrics, PLoS ONE and Proceedings of the IEEE.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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