Markus Lang

2.3k citations
74 papers · 1.6k · h-index 19

Impact in

Papers in

Markus Lang

68 papers receiving 1.5k citations

Peers

Markus Lang
Comparison fields: 5 of 129
  • Signal Processing 549
  • Computer Vision and Pattern Recognition 784
  • Media Technology 302
  • Computational Mechanics 429
  • Metals and Alloys 21
Replace Magdalena Salazar‐Palma with:
Magdalena Salazar‐Palma Spain
M. Ibrahim Sezan United States
NG Kingsbury United Kingdom
Qingtang Jiang United States
N. Kingsbury United Kingdom
Dapang Chen United States
Naoki Saito United States
C.W. Therrien United States
G.L. Wise United States
Jean-Christophe Feauveau France
Markus Lang relative to Magdalena Salazar‐Palma Spain Magdalena Salazar‐Palma's profile →
Citations per field
00.5×8.6×
Magdalena Salazar‐Palma · 1×
Citations per year

Countries citing papers authored by Markus Lang

Since Specialization
Citations

This map shows the geographic impact of Markus Lang'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 Markus Lang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Lang more than expected).

Fields of papers citing papers by Markus Lang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Lang. 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 Markus Lang. The network helps show where Markus Lang may publish in the future.

Co-authors

The 25 scholars most cited alongside Markus Lang, 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 Markus Lang Line = papers co-authored together Markus Lang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1996362
2 2002142
3 2000120
4 199696
5 199484
6 199568
7 199462
8 199860
9 200640
10 199936
11 199534
12 199627
13 199225
14 199423
15 199822
16 200222
17 199421
18 201720
19 201320
20 199817

About Markus Lang

Markus Lang is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics, Electrical and Electronic Engineering and Media Technology, having authored 74 papers that have together received 1.6k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (27 papers), Digital Filter Design and Implementation (25 papers), Advanced Adaptive Filtering Techniques (20 papers), Advanced Image Fusion Techniques (8 papers), Advanced Data Compression Techniques (7 papers), Fatigue and fracture mechanics (6 papers), Sparse and Compressive Sensing Techniques (5 papers) and Advanced Image Processing Techniques (5 papers). The work is most often cited by research in Signal Processing (549 citations), Computer Vision and Pattern Recognition (784 citations), Media Technology (302 citations), Computational Mechanics (429 citations) and Metals and Alloys (21 citations). Markus Lang has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include C.S. Burrus, Jan E. Odegard, Raymond O. Wells, Ivan Selesnick, Haitao Guo, T.I. Laakso, R. Gopinath, Joachim Bamberger, Klaus Sarimski and Manfred Hintermair. Their work appears in journals such as IEEE Transactions on Signal Processing, Signal Processing, Fatigue & Fracture of Engineering Materials & Structures, IEEE Signal Processing Letters and IEEE Transactions on Image Processing.

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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