Fatoş T. Yarman-Vural

1.0k total citations
32 papers, 668 citations indexed

About

Fatoş T. Yarman-Vural is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Fatoş T. Yarman-Vural has authored 32 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 4 papers in Media Technology. Recurrent topics in Fatoş T. Yarman-Vural's work include Image Retrieval and Classification Techniques (14 papers), Handwritten Text Recognition Techniques (10 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Fatoş T. Yarman-Vural is often cited by papers focused on Image Retrieval and Classification Techniques (14 papers), Handwritten Text Recognition Techniques (10 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Fatoş T. Yarman-Vural collaborates with scholars based in Türkiye, United States and Japan. Fatoş T. Yarman-Vural's co-authors include Nafiz Arıca, Erhan Öztop, Volkan Atalay, Mete Özay, Emre Akbaş, Pınar Duygulu, Serkan Genç, Ahmet Sayar, Çağlar Şenaras and A. Murat Tekalp and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Pattern Recognition Letters.

In The Last Decade

Fatoş T. Yarman-Vural

31 papers receiving 545 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fatoş T. Yarman-Vural Türkiye 9 603 295 177 46 22 32 668
Kazuki Nakashima Japan 3 521 0.9× 161 0.5× 214 1.2× 39 0.8× 29 1.3× 10 607
Tetsushi Wakabayashi Japan 11 491 0.8× 204 0.7× 194 1.1× 45 1.0× 15 0.7× 77 581
A. P. SHIVAPRASAD India 5 494 0.8× 200 0.7× 124 0.7× 58 1.3× 19 0.9× 33 588
Yasuji Miyake Japan 8 571 0.9× 167 0.6× 184 1.0× 26 0.6× 28 1.3× 12 627
Da-Han Wang China 9 716 1.2× 209 0.7× 282 1.6× 77 1.7× 43 2.0× 18 768
S. Knerr France 9 343 0.6× 81 0.3× 239 1.4× 63 1.4× 27 1.2× 12 446
Kwang In Kim Germany 7 634 1.1× 194 0.7× 71 0.4× 9 0.2× 24 1.1× 8 696
Jon Almazán Spain 11 1.6k 2.6× 449 1.5× 436 2.5× 36 0.8× 44 2.0× 13 1.6k
Christian Viard-Gaudin France 13 662 1.1× 144 0.5× 312 1.8× 93 2.0× 47 2.1× 43 745
Mohammad Tanvir Parvez Saudi Arabia 12 640 1.1× 175 0.6× 257 1.5× 68 1.5× 44 2.0× 41 732

Countries citing papers authored by Fatoş T. Yarman-Vural

Since Specialization
Citations

This map shows the geographic impact of Fatoş T. Yarman-Vural'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 Fatoş T. Yarman-Vural with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fatoş T. Yarman-Vural more than expected).

Fields of papers citing papers by Fatoş T. Yarman-Vural

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fatoş T. Yarman-Vural. 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 Fatoş T. Yarman-Vural. The network helps show where Fatoş T. Yarman-Vural may publish in the future.

Co-authorship network of co-authors of Fatoş T. Yarman-Vural

This figure shows the co-authorship network connecting the top 25 collaborators of Fatoş T. Yarman-Vural. A scholar is included among the top collaborators of Fatoş T. Yarman-Vural 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 Fatoş T. Yarman-Vural. Fatoş T. Yarman-Vural 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
1.
Kalkan, Sinan, et al.. (2025). L-VAE: variational auto-encoder with learnable beta for disentangled representation. Machine Vision and Applications. 36(5).
2.
Yarman-Vural, Fatoş T., et al.. (2015). Classification of fMRI data by using clustering. 2381–2383. 1 indexed citations
3.
Yarman-Vural, Fatoş T., et al.. (2015). Ensembling brain regions for brain decoding. PubMed. 293. 2948–2951. 1 indexed citations
4.
Özay, Mete & Fatoş T. Yarman-Vural. (2015). Hierarchical distance learning by stacking nearest neighbor classifiers. Information Fusion. 29. 14–31. 5 indexed citations
5.
Özay, Mete & Fatoş T. Yarman-Vural. (2012). A New Fuzzy Stacked Generalization Technique for Deep learning and Analysis of its Performance. arXiv (Cornell University). 4 indexed citations
6.
Yarman-Vural, Fatoş T., et al.. (2007). A pattern classification approach for boosting with genetic algorithms. OpenMETU (Middle East Technical University). 5 indexed citations
7.
Yarman-Vural, Fatoş T., et al.. (2004). A content based image retrieval system based on the fuzzy ARTMAP architecture. 248–251. 2 indexed citations
8.
Genç, Serkan & Fatoş T. Yarman-Vural. (2003). Morphing as a tool for motion modelling. 538–543. 1 indexed citations
9.
Arıca, Nafiz & Fatoş T. Yarman-Vural. (2002). One dimensional representation of two dimensional information for HMM based handwritten recognition. 2. 948–952. 4 indexed citations
10.
Öztop, Erhan, et al.. (2002). Repulsive attractive network for baseline extraction on document images. 4. 3181–3184. 3 indexed citations
11.
Arıca, Nafiz & Fatoş T. Yarman-Vural. (2001). An overview of character recognition focused on off-line handwriting. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 31(2). 216–233. 335 indexed citations
12.
Duygulu, Pınar & Fatoş T. Yarman-Vural. (2001). <title>Multilevel image segmentation and object representation for content-based image retrieval</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4315. 460–469. 2 indexed citations
13.
Arıca, Nafiz & Fatoş T. Yarman-Vural. (2000). One-dimensional representation of two-dimensional information for HMM based handwriting recognition. Pattern Recognition Letters. 21(6-7). 583–592. 11 indexed citations
14.
Arıca, Nafiz & Fatoş T. Yarman-Vural. (1999). A new HMM topology for shape recognition.. OpenMETU (Middle East Technical University). 756–760. 2 indexed citations
15.
Öztop, Erhan, et al.. (1999). Repulsive attractive network for baseline extraction on document images. Signal Processing. 75(1). 1–10. 30 indexed citations
16.
Yarman-Vural, Fatoş T., et al.. (1997). A heuristic algorithm for optical character recognition of Arabic script. Signal Processing. 62(1). 87–99. 33 indexed citations
17.
Yarman-Vural, Fatoş T., et al.. (1997). <title>Set of texture similarity measures</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3029. 118–127. 2 indexed citations
18.
Yarman-Vural, Fatoş T., et al.. (1996). <title>Heuristic algorithm for optical character recognition of Arabic script</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2727. 725–736. 11 indexed citations
19.
Yarman-Vural, Fatoş T., et al.. (1989). The generalized H-lattice filter and estimation of the ARMA models. IEEE Transactions on Acoustics Speech and Signal Processing. 37(1). 147–151. 2 indexed citations
20.
Yarman-Vural, Fatoş T., et al.. (1988). Modified condensed nearest neighbor rule as applied to speaker independent word recognition. Speech Communication. 7(4). 411–415. 3 indexed citations

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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