M. Gorgoń

3.3k citations
51 papers · 594 · h-index 15

Impact in

Papers in

M. Gorgoń

48 papers receiving 565 citations

Peers

M. Gorgoń
Comparison fields: 5 of 94
  • Computer Vision and Pattern Recognition 339
  • Hardware and Architecture 47
  • Media Technology 51
  • Artificial Intelligence 131
  • Biophysics 22
Replace Roman Solovyev with:
Roman Solovyev Russia
Xiaofeng Mao China
Mattias O’Nils Sweden
Ajay J. Joshi United States
Zoltán Vámossy Hungary
Guoqing Jin China
Jingye Cai China
Xiaotian Wang China
Weichao Qiu United States
Alexandre Sablayrolles Israel
M. Gorgoń relative to Roman Solovyev Russia Roman Solovyev's profile →
Citations per field
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Roman Solovyev · 1×
Citations per year

Countries citing papers authored by M. Gorgoń

Since Specialization
Citations

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

Fields of papers citing papers by M. Gorgoń

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside M. Gorgoń, 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 M. Gorgoń Line = papers co-authored together M. Gorgoń links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201961
2 201940
3 201236
4 202029
5 202026
6 201425
7 201224
8 201422
9 202020
10 201620
11 201118
12
Real-time implementation of the ViBe foreground object segmentation algorithm
201317
13 200416
14
FPGA implementation of real-time head-shoulder detection using local binary patterns, SVM and foreground object detection
201216
15 201415
16 201514
17 201213
18 200912
19 200712
20 202111

About M. Gorgoń

M. Gorgoń is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Artificial Intelligence, Aerospace Engineering and Signal Processing, having authored 51 papers that have together received 594 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (14 papers), Video Surveillance and Tracking Methods (12 papers), CCD and CMOS Imaging Sensors (10 papers), Advanced Data Compression Techniques (9 papers), Infrared Target Detection Methodologies (7 papers), Video Coding and Compression Technologies (6 papers), Advanced Image and Video Retrieval Techniques (5 papers) and AI in cancer detection (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (339 citations), Hardware and Architecture (47 citations), Media Technology (51 citations), Artificial Intelligence (131 citations) and Biophysics (22 citations). M. Gorgoń has collaborated with scholars based in Poland, Brazil and United States. Frequent co-authors include Tomasz Kryjak, Joanna Jaworek-Korjakowska, M. Jabłoński, Grzegorz Dyduch, P. Pawlik, Paweł Skruch, Andrzej Brodzicki, Ryszard Tadeusiewicz, Vanderlei Bonato and Christos-Savvas Bouganis. Their work appears in journals such as Sensors, Journal of Systems Architecture, International Journal of Applied Mathematics and Computer Science, Computerized Medical Imaging and Graphics and Measurement.

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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