Rigas Kouskouridas

1.1k total citations
20 papers, 477 citations indexed

About

Rigas Kouskouridas is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Rigas Kouskouridas has authored 20 papers receiving a total of 477 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 12 papers in Aerospace Engineering and 4 papers in Control and Systems Engineering. Recurrent topics in Rigas Kouskouridas's work include Robotics and Sensor-Based Localization (12 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Advanced Vision and Imaging (5 papers). Rigas Kouskouridas is often cited by papers focused on Robotics and Sensor-Based Localization (12 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Advanced Vision and Imaging (5 papers). Rigas Kouskouridas collaborates with scholars based in Greece, United Kingdom and Singapore. Rigas Kouskouridas's co-authors include Αντώνιος Γαστεράτος, Tae‐Kyun Kim, Andreas Doumanoglou, Sotiris Malassiotis, Vassilios Vonikakis, Dimitrios Chrysostomou, Caner Şahin, Alykhan Tejani, Danhang Tang and Vassileios Balntas and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and Neurocomputing.

In The Last Decade

Rigas Kouskouridas

20 papers receiving 450 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rigas Kouskouridas Greece 10 375 230 156 57 44 20 477
Fred Rothganger United States 8 428 1.1× 263 1.1× 156 1.0× 40 0.7× 28 0.6× 18 548
Yonggen Ling China 10 308 0.8× 127 0.6× 53 0.3× 41 0.7× 32 0.7× 30 372
Jean-Sébastien Franco France 14 578 1.5× 192 0.8× 34 0.2× 39 0.7× 65 1.5× 38 662
Muriel Pressigout France 10 588 1.6× 268 1.2× 39 0.3× 97 1.7× 68 1.5× 18 655
Christophe Collewet France 9 396 1.1× 113 0.5× 41 0.3× 162 2.8× 48 1.1× 33 487
Robert B. Kelley United States 12 339 0.9× 142 0.6× 132 0.8× 71 1.2× 30 0.7× 40 480
Tomáš Hodaň United States 8 390 1.0× 276 1.2× 294 1.9× 12 0.2× 70 1.6× 10 545
Tanner Schmidt United States 9 382 1.0× 142 0.6× 126 0.8× 11 0.2× 56 1.3× 11 496
Ezio Malis France 12 459 1.2× 317 1.4× 49 0.3× 123 2.2× 26 0.6× 19 538
Junyi Geng United States 9 206 0.5× 95 0.4× 113 0.7× 41 0.7× 7 0.2× 25 363

Countries citing papers authored by Rigas Kouskouridas

Since Specialization
Citations

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

Fields of papers citing papers by Rigas Kouskouridas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rigas Kouskouridas

This figure shows the co-authorship network connecting the top 25 collaborators of Rigas Kouskouridas. A scholar is included among the top collaborators of Rigas Kouskouridas 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 Rigas Kouskouridas. Rigas Kouskouridas 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.
Li, Shuda, et al.. (2021). X Resolution Correspondence Networks. 3 indexed citations
2.
Tejani, Alykhan, Rigas Kouskouridas, Andreas Doumanoglou, Danhang Tang, & Tae‐Kyun Kim. (2017). Latent-Class Hough Forests for 6 DoF Object Pose Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(1). 119–132. 44 indexed citations
3.
Vonikakis, Vassilios, Rigas Kouskouridas, & Αντώνιος Γαστεράτος. (2017). On the evaluation of illumination compensation algorithms. Multimedia Tools and Applications. 77(8). 9211–9231. 67 indexed citations
4.
Şahin, Caner, Rigas Kouskouridas, & Tae‐Kyun Kim. (2017). A learning-based variable size part extraction architecture for 6D object pose recovery in depth images. Image and Vision Computing. 63. 38–50. 10 indexed citations
5.
Balntas, Vassileios, Andreas Doumanoglou, Caner Şahin, et al.. (2017). Pose Guided RGBD Feature Learning for 3D Object Pose Estimation. 3876–3884. 48 indexed citations
6.
Doumanoglou, Andreas, Rigas Kouskouridas, Sotiris Malassiotis, & Tae‐Kyun Kim. (2016). Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd. 3583–3592. 134 indexed citations
7.
Şahin, Caner, Rigas Kouskouridas, & Tae‐Kyun Kim. (2016). Iterative Hough Forest with Histogram of Control Points for 6 DoF object registration from depth images. 4113–4118. 9 indexed citations
8.
Kouskouridas, Rigas, et al.. (2015). Video-based object recognition with weakly supervised object localization. 46–50. 2 indexed citations
9.
Kouskouridas, Rigas, Angelos Amanatiadis, Savvas A. Chatzichristofis, & Αντώνιος Γαστεράτος. (2015). What, Where and How? Introducing pose manifolds for industrial object manipulation. Expert Systems with Applications. 42(21). 8123–8133. 4 indexed citations
10.
Kouskouridas, Rigas, et al.. (2014). Sparse pose manifolds. Autonomous Robots. 37(2). 191–207. 5 indexed citations
11.
Kouskouridas, Rigas, Αντώνιος Γαστεράτος, & Christos Emmanouilidis. (2013). Efficient representation and feature extraction for neural network-based 3D object pose estimation. Neurocomputing. 120. 90–100. 7 indexed citations
12.
Vonikakis, Vassilios, Rigas Kouskouridas, & Αντώνιος Γαστεράτος. (2013). A comparison framework for the evaluation of illumination compensation algorithms. 2. 264–268. 3 indexed citations
13.
Chrysostomou, Dimitrios, et al.. (2013). A biologically inspired scale-space for illumination invariant feature detection. Measurement Science and Technology. 24(7). 74024–74024. 32 indexed citations
14.
Vonikakis, Vassilios, Dimitrios Chrysostomou, Rigas Kouskouridas, & Αντώνιος Γαστεράτος. (2012). Improving the robustness in feature detection by local contrast enhancement. 25 indexed citations
15.
Kouskouridas, Rigas, Konstantinos Charalampous, & Αντώνιος Γαστεράτος. (2012). 6DoF object pose measurement by a monocular manifold-based pattern recognition technique. Measurement Science and Technology. 23(11). 114005–114005. 7 indexed citations
16.
Chrysostomou, Dimitrios, et al.. (2012). Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '12). 35 indexed citations
17.
Kouskouridas, Rigas, et al.. (2012). Evaluation of two-part algorithms for objects’ depth estimation. IET Computer Vision. 6(1). 70–78. 7 indexed citations
18.
Kouskouridas, Rigas, et al.. (2012). Ontology-based 3D pose estimation for autonomous object manipulation. 5. 476–481. 2 indexed citations
19.
Kouskouridas, Rigas, Angelos Amanatiadis, & Αντώνιος Γαστεράτος. (2011). Guiding a robotic gripper by visual feedback for object manipulation tasks. 433–438. 14 indexed citations
20.
Stépàn, Gábor, András József Tóth, Gunnar Bolmsjö, et al.. (2009). ACROBOTER: a ceiling based crawling, hoisting and swinging service robot platform. 19 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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