S. Ghebreab

962 total citations
40 papers, 628 citations indexed

About

S. Ghebreab is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, S. Ghebreab has authored 40 papers receiving a total of 628 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 19 papers in Cognitive Neuroscience and 3 papers in Artificial Intelligence. Recurrent topics in S. Ghebreab's work include Visual perception and processing mechanisms (14 papers), Neural dynamics and brain function (13 papers) and Visual Attention and Saliency Detection (7 papers). S. Ghebreab is often cited by papers focused on Visual perception and processing mechanisms (14 papers), Neural dynamics and brain function (13 papers) and Visual Attention and Saliency Detection (7 papers). S. Ghebreab collaborates with scholars based in Netherlands, United States and Slovakia. S. Ghebreab's co-authors include H. Steven Scholte, Victor A. F. Lamme, A.W.M. Smeulders, Iris I. A. Groen, Lourens Waldorp, Theo Gevers, Roberto Valenti, Harrie C. M. Vorst, C. Carl Jaffe and Sara Jahfari and has published in prestigious journals such as Journal of Neuroscience, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Neurophysiology.

In The Last Decade

S. Ghebreab

35 papers receiving 613 citations

Peers

S. Ghebreab

CVPR

Graham K Edgar United Kingdom

J. Najemnik United States

Guido Maiello Germany

Laura Renninger United States

Denis Pellerin France

Jeffrey S. Perry United States

Steven A. Cholewiak United States

Jacqueline M. Fulvio United States

Anne Guérin-Dugué France

Agostino Gibaldi Italy

Graham K Edgar United Kingdom

S. Ghebreab

416 ×1.2

121 ×0.4

CVPR

59 ×0.8

54 ×1.1

10 ×0.2

Citations per year, relative to S. Ghebreab S. Ghebreab (= 1×) peers Graham K Edgar

Countries citing papers authored by S. Ghebreab

Since Specialization

Citations

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

Fields of papers citing papers by S. Ghebreab

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Ghebreab

This figure shows the co-authorship network connecting the top 25 collaborators of S. Ghebreab. A scholar is included among the top collaborators of S. Ghebreab 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 S. Ghebreab. S. Ghebreab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ghebreab, S., et al.. (2024). Strategic manipulation of preferences in the rank minimization mechanism. Autonomous Agents and Multi-Agent Systems. 38(2).

Ghebreab, S., et al.. (2024). Roles of Standardised Criteria in Assessing Societal Impact of AI. UvA-DARE (University of Amsterdam). 1240–1245.

Ghebreab, S., et al.. (2024). Perceptive Visual Urban Analytics is Not (Yet) Suitable for Municipalities. UvA-DARE (University of Amsterdam). 1341–1354.

Groen, Iris I. A., et al.. (2018). Scene complexity modulates degree of feedback activity during object detection in natural scenes. PLoS Computational Biology. 14(12). e1006690–e1006690. 22 indexed citations

Vorst, Harrie C. M., et al.. (2016). Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia. NeuroImage Clinical. 11. 508–514. 53 indexed citations

Zeman, Astrid, Kevin R. Brooks, & S. Ghebreab. (2015). An exponential filter model predicts lightness illusions. Frontiers in Human Neuroscience. 9. 368–368. 6 indexed citations

Scholte, H. Steven, et al.. (2015). Visual dictionaries as intermediate features in the human brain. Frontiers in Computational Neuroscience. 8. 168–168. 8 indexed citations

Groen, Iris I. A., S. Ghebreab, Victor A. F. Lamme, & H. Steven Scholte. (2013). Two stages in scene gist processing revealed by evaluating summary statistics with single-image ERPs. Journal of Vision. 13(9). 1059–1059. 1 indexed citations

Scholte, H. Steven, Ilja G. Sligte, Iris I. A. Groen, Victor A. F. Lamme, & S. Ghebreab. (2013). The posterior part of the lateral occipital complex analyzes the spatial correlation structure of natural visual scenes.. Journal of Vision. 13(9). 1098–1098. 2 indexed citations

10.

Groen, Iris I. A., et al.. (2013). From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category. Journal of Neuroscience. 33(48). 18814–18824. 93 indexed citations

11.

Groen, Iris I. A., S. Ghebreab, Victor A. F. Lamme, & H. Steven Scholte. (2012). Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories. PLoS Computational Biology. 8(10). e1002726–e1002726. 50 indexed citations

12.

Groen, Iris I. A., S. Ghebreab, Victor A. F. Lamme, & H. Steven Scholte. (2012). Low-Level Edge Statistics Predict Invariance of Natural Textures. Frontiers in Computational Neuroscience. 6(34). 1 indexed citations

13.

Groen, Iris I. A., S. Ghebreab, Victor A. F. Lamme, & H. Steven Scholte. (2012). Low-level contrast statistics are diagnostic of invariance of natural textures. Frontiers in Computational Neuroscience. 6. 34–34. 20 indexed citations

14.

Ghebreab, S., et al.. (2009). A Biologically Plausible Model for Rapid Natural Scene Identification. UvA-DARE (University of Amsterdam). 22. 629–637. 20 indexed citations

15.

Scholte, H. Steven, S. Ghebreab, Lourens Waldorp, A.W.M. Smeulders, & Victor A. F. Lamme. (2009). Brain responses strongly correlate with Weibull image statistics when processing natural images. Journal of Vision. 9(4). 29–29. 142 indexed citations

16.

Ghebreab, S., A.W.M. Smeulders, & Pieter Adriaans. (2007). Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression. UvA-DARE (University of Amsterdam). 20. 537–544. 6 indexed citations

17.

Ghebreab, S., A.W.M. Smeulders, & Pieter Adriaans. (2007). Predictive Modeling of fMRI Brain States Using Functional Canonical Correlation Analysis. Artificial Intelligence in Medicine. 393–397. 3 indexed citations

18.

Ghebreab, S. & A.W.M. Smeulders. (2005). An approximately complete string representation of local object boundary features for concept-based biomedical image retrieval. 2. 1159–1162. 1 indexed citations

19.

Ghebreab, S. & A.W.M. Smeulders. (2004). Combining Strings and Necklaces for Interactive Three-Dimensional Segmentation of Spinal Images Using an Integral Deformable Spine Model. IEEE Transactions on Biomedical Engineering. 51(10). 1821–1829. 28 indexed citations

20.

Ghebreab, S. & A.W.M. Smeulders. (2004). An Approximately Complete String Representation of Local Object Boundary Features for Concept-Based Image Retrieval. UvA-DARE (University of Amsterdam). 1 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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