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.
Fast approximate energy minimization via graph cuts
20014.8k citationsYuri Boykov, Olga Veksler et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
20043.1k citationsYuri Boykov, Vladimir KolmogorovIEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
This map shows the geographic impact of Yuri Boykov'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 Yuri Boykov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuri Boykov more than expected).
This network shows the impact of papers produced by Yuri Boykov. 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 Yuri Boykov. The network helps show where Yuri Boykov may publish in the future.
Co-authorship network of co-authors of Yuri Boykov
This figure shows the co-authorship network connecting the top 25 collaborators of Yuri Boykov.
A scholar is included among the top collaborators of Yuri Boykov 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 Yuri Boykov. Yuri Boykov 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.
Veksler, Olga & Yuri Boykov. (2024). Sparse Non-Local CRF With Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(2). 773–788.1 indexed citations
Olsson, Carl, et al.. (2013). In Defense of 3D-Label Stereo. Lund University Publications (Lund University). 1730–1737.35 indexed citations
10.
Delong, Andrew, Olga Veksler, Anton Osokin, & Yuri Boykov. (2012). Minimizing Sparse High-Order Energies by Submodular Vertex-Cover. 25. 962–970.10 indexed citations
11.
Cremers, Daniel, Yuri Boykov, A. Blake, & Frank Schmidt. (2009). Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition.2 indexed citations
12.
Cremers, Daniel, Yuri Boykov, A. Blake, & Frank Schmidt. (2009). Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, ... Vision, Pattern Recognition, and Graphics. Springer eBooks.1 indexed citations
13.
Boykov, Yuri, et al.. (2009). Energy Minimization Methods for Computer Vision and Pattern Recognition (EMMCVPR).5 indexed citations
Boykov, Yuri, et al.. (2006). Active Graph Cuts. 1. 1023–1029.68 indexed citations
16.
Boykov, Yuri & Vladimir Kolmogorov. (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(9). 1124–1137.3120 indexed citations breakdown →
Boykov, Yuri, Olga Veksler, & Ramin Zabih. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23(11). 1222–1239.4750 indexed citations breakdown →
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.