Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Recent progress in road and lane detection: a survey
2012544 citationsAharon Bar-Hillel, Dan Levi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Aharon Bar-Hillel
Since
Specialization
Citations
This map shows the geographic impact of Aharon Bar-Hillel'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 Aharon Bar-Hillel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aharon Bar-Hillel more than expected).
Fields of papers citing papers by Aharon Bar-Hillel
This network shows the impact of papers produced by Aharon Bar-Hillel. 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 Aharon Bar-Hillel. The network helps show where Aharon Bar-Hillel may publish in the future.
Co-authorship network of co-authors of Aharon Bar-Hillel
This figure shows the co-authorship network connecting the top 25 collaborators of Aharon Bar-Hillel.
A scholar is included among the top collaborators of Aharon Bar-Hillel 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 Aharon Bar-Hillel. Aharon Bar-Hillel is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Biess, Armin, et al.. (2018). Learning a High-Precision Robotic Assembly Task Using Pose Estimation from Simulated Depth Images.. arXiv (Cornell University).2 indexed citations
11.
Bar-Hillel, Aharon, et al.. (2018). Leaf counting: Multiple scale regression and detection using deep CNNs.. British Machine Vision Conference. 328.29 indexed citations
Bar-Hillel, Aharon, Tomer Hertz, Noam Shental, & Daphna Weinshall. (2005). Learning a Mahalanobis Metric from Equivalence Constraints. Journal of Machine Learning Research. 6(32). 937–965.361 indexed citations
17.
Bar-Hillel, Aharon, Adam Spiro, & Eran Stark. (2004). Spike Sorting: Bayesian Clustering of Non-Stationary Data. Neural Information Processing Systems. 17. 105–112.5 indexed citations
18.
Shental, Noam, Aharon Bar-Hillel, Tomer Hertz, & Daphna Weinshall. (2003). Computing Gaussian Mixture Models with EM Using Side-Information.14 indexed citations
19.
Shental, Noam, Aharon Bar-Hillel, Tomer Hertz, & Daphna Weinshall. (2003). Computing Gaussian Mixture Models with EM Using Equivalence Constraints. Neural Information Processing Systems. 16. 465–472.173 indexed citations
20.
Bar-Hillel, Aharon, Tomer Hertz, Noam Shental, & Daphna Weinshall. (2003). Learning distance functions using equivalence relations. International Conference on Machine Learning. 11–18.281 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.