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.
Learning multi-label scene classification
20041.6k citationsMatthew Boutell, Jiebo Luo 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 Christopher M. Brown
Since
Specialization
Citations
This map shows the geographic impact of Christopher M. Brown'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 Christopher M. Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher M. Brown more than expected).
Fields of papers citing papers by Christopher M. Brown
This network shows the impact of papers produced by Christopher M. Brown. 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 Christopher M. Brown. The network helps show where Christopher M. Brown may publish in the future.
Co-authorship network of co-authors of Christopher M. Brown
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher M. Brown.
A scholar is included among the top collaborators of Christopher M. Brown 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 Christopher M. Brown. Christopher M. Brown is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Brown, Christopher M., et al.. (2017). An examination of sport management master's programs in the United States.. The Sport Journal. 20.4 indexed citations
Barnum, Peter, Bo Hu, & Christopher M. Brown. (2003). Exploring the Practical Limits of Optical Flow. The Journal of Clinical Psychiatry. 65 Suppl 12. 3–4.2 indexed citations
Brown, Christopher M. & Demetri Terzopoulos. (1995). Real-time computer vision. Electronics & Communications Engineering Journal. 8(6). 278–278.37 indexed citations
12.
Black, Michael J., Yiannis Aloimonos, Christopher M. Brown, et al.. (1993). Action Representation and Purpose: Re-evaluating the Foundations of Computational Vision.. International Joint Conference on Artificial Intelligence. 1661–1666.2 indexed citations
13.
Brown, Christopher M.. (1992). Numerical evaluation of differential and semi-differential invariants. UR Research (University of Rochester). 215–227.8 indexed citations
14.
Chou, Paul B. & Christopher M. Brown. (1987). Probabilistic information fusion for multi-modal image segmentation. International Joint Conference on Artificial Intelligence. 779–782.6 indexed citations
15.
Brown, Christopher M.. (1987). Advances in Computer Vision. CERN Document Server (European Organization for Nuclear Research).98 indexed citations
16.
Brown, Christopher M., D.H. Ballard, & Owen Kimball. (1983). Constraint interaction in shape-from-shading algorithms. 261–299.6 indexed citations
17.
Ballard, Dana H., et al.. (1983). BOUNDARY CONDITIONS IN MULTIPLE INTRINSIC IMAGES.. International Joint Conference on Artificial Intelligence. 1068–1072.1 indexed citations
18.
Brown, Christopher M., et al.. (1983). Advanced Hough Transform Implementations. International Joint Conference on Artificial Intelligence. 1081–1085.11 indexed citations
Ballard, D.H., Christopher M. Brown, & Jerome A. Feldman. (1977). An approach to knowledge-directed image analysis. UR Research (University of Rochester). 664–670.32 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.