Bryan Usevitch

683 citations
23 papers · 498 · h-index 8

Impact in

    • Image and Signal Denoising Methods
    • Advanced Data Compression Techniques
    • Advanced Vision and Imaging
    • Advanced Image Processing Techniques
    • Digital Filter Design and Implementation
    • Video Coding and Compression Technologies
    • Blind Source Separation Techniques

Papers in

Bryan Usevitch

21 papers receiving 452 citations

Peers

Bryan Usevitch
Comparison fields: 5 of 57
  • Computer Vision and Pattern Recognition 408
  • Signal Processing 207
  • Media Technology 60
  • Computer Graphics and Computer-Aided Design 18
  • Computational Mechanics 51
Replace Michael Gormish with:
Michael Gormish United States
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Citations per field
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Citations per year

Countries citing papers authored by Bryan Usevitch

Since Specialization
Citations

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

Fields of papers citing papers by Bryan Usevitch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside Bryan Usevitch, 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 Bryan Usevitch Line = papers co-authored together Bryan Usevitch links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2001312
2 200264
3 199521
4 200320
5 199620
6 20068
7 20028
8 20078
9 19996
10 20055
11 20184
12 20054
13 20053
14 20063
15 20043
16 20162
17
Perfect Reconstruction Filter Banks for Adaptive Filtering and Coding
19932
18 20032
19 20161
20 20031

About Bryan Usevitch

Bryan Usevitch is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics, Computational Theory and Mathematics and Media Technology, having authored 23 papers that have together received 498 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (18 papers), Advanced Data Compression Techniques (13 papers), Digital Filter Design and Implementation (12 papers), Image Retrieval and Classification Techniques (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Advanced Adaptive Filtering Techniques (2 papers), Numerical Methods and Algorithms (1 paper) and Advanced Optical Network Technologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (408 citations), Signal Processing (207 citations), Media Technology (60 citations), Computer Graphics and Computer-Aided Design (18 citations) and Computational Mechanics (51 citations). Bryan Usevitch has collaborated with scholars based in United States. Frequent co-authors include M.T. Orchard, Olga Kosheleva, Sergio D. Cabrera, E. Vidal, Владик Крейнович, Mehdi Shadaram, D. Lafaw, Vikram Jayaram, Apurva N. Mody and W.K. Jenkins. Their work appears in journals such as IEEE Signal Processing Magazine, IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing, EURASIP Journal on Image and Video Processing and Studies in fuzziness and soft computing.

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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