Andrew G. Tescher

721 citations
70 papers · 454 indexed · h-index 9
Topics
Image and Signal Denoising Methods (24 papers)Advanced Data Compression Techniques (24 papers)Advanced Image Fusion Techniques (6 papers)

In The Last Decade

Andrew G. Tescher

63 papers receiving 402 citations

Peers

Andrew G. Tescher
Comparison fields: 5 of 76
  • Computer Vision and Pattern Recognition 364
  • Media Technology 105
  • Signal Processing 98
  • Artificial Intelligence 50
  • Electrical and Electronic Engineering 39
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Countries citing papers authored by Andrew G. Tescher

Since Specialization
Citations

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

Fields of papers citing papers by Andrew G. Tescher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew G. Tescher. 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 Andrew G. Tescher. The network helps show where Andrew G. Tescher may publish in the future.

Co-authorship network of co-authors of Andrew G. Tescher

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew G. Tescher. A scholar is included among the top collaborators of Andrew G. Tescher based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew G. Tescher. Andrew G. Tescher is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Applications of Digital Image Processing XXXVII
7
2
Applications of Digital Image Processing XXXII
3
3 5
4
Applications of Digital Image Processing XXIX
2
5
Applications of digital image processing XXIX : 15-17 August 2006, San Diego, California, USA
1
6
Applications of Digital Image Processing XXVIII
2
7 2
8
Applications of Digital Image Processing XXII
4
9 1
10
Selected Papers on Visual Communication: Technology and Applications
1
11
Feature-based image bandwidth compression
1
12
Hybrid Image and Signal Processing
4
13 2
14
Advances in Image Processing
6
15
Hybrid image processing
2
16 1
17
Applications of digital image processing III
4
18
Applications of Digital Image Processing
4
19
Transform Image Coding.
31
20 91

About Andrew G. Tescher

Andrew G. Tescher is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 70 papers that have together received 454 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (24 papers), Advanced Data Compression Techniques (24 papers) and Advanced Image Fusion Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (364 citations), Media Technology (105 citations) and Signal Processing (98 citations). Andrew G. Tescher has collaborated with scholars based in United States, Kuwait and Germany. Frequent co-authors include John A. Saghri, Harry C. Andrews, R. P. Kruger, Fréderic Dufaux, F. Simone, Touradj Ebrahimi, Mourad Ouaret, J. R. Parsons, David Casasent and A. Oosterlinck. Their work appears in journals such as IEEE Signal Processing Magazine, Optical Engineering and IEEE Spectrum.

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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