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.
Visual learning and recognition of 3-d objects from appearance
19951.3k citationsHiroshi Murase, Shree K. Nayarprofile →
Contrast restoration of weather degraded images
20031.2k citationsShree K. Nayar et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Countries citing papers authored by Shree K. Nayar
Since
Specialization
Citations
This map shows the geographic impact of Shree K. Nayar'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 Shree K. Nayar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shree K. Nayar more than expected).
This network shows the impact of papers produced by Shree K. Nayar. 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 Shree K. Nayar. The network helps show where Shree K. Nayar may publish in the future.
Co-authorship network of co-authors of Shree K. Nayar
This figure shows the co-authorship network connecting the top 25 collaborators of Shree K. Nayar.
A scholar is included among the top collaborators of Shree K. Nayar 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 Shree K. Nayar. Shree K. Nayar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kuthirummal, Sujit, Hajime Nagahara, Changyin Zhou, & Shree K. Nayar. (2010). Flexible Depth of Field Photography. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(1). 58–71.91 indexed citations
4.
Kumar, Neeraj, Alexander C. Berg, Peter N. Belhumeur, & Shree K. Nayar. (2009). Attribute and simile classifiers for face verification. 365–372.940 indexed citations breakdown →
5.
Nayar, Shree K. & Sujit Kuthirummal. (2009). Flexible imaging for capturing depth and controlling field of view and depth of field.5 indexed citations
Nayar, Shree K. & Vlad Branzoi. (2003). Adaptive dynamic range imaging: Optical control of pixel exposures over space and time.22 indexed citations
13.
Ben-Ezra, Moshe & Shree K. Nayar. (2003). What Does Motion Reveal About Transparency. 1025–1032.13 indexed citations
14.
Nayar, Shree K. & Tomoo Mitsunaga. (2000). High dynamic range imaging: Spatially varying pixel exposures.
15.
Nayar, Shree K. & Terrance E. Boult. (1998). Omnidirectional Vision Systems: 1998 PI Report.10 indexed citations
16.
Swaminathan, Rahul & Shree K. Nayar. (1998). Non-Metric Calibration of Wide Angle Lenses.3 indexed citations
17.
Murase, Hiroshi & Shree K. Nayar. (1993). Learning object models from appearance. National Conference on Artificial Intelligence. 836–843.25 indexed citations
18.
Nayar, Shree K.. (1992). Shape from focus system for rough surfaces. 347–360.11 indexed citations
19.
Nayar, Shree K., et al.. (1992). Implementation and Evaluation of a Three-Dimensional Photometric Sampler. Defense Technical Information Center (DTIC). 93. 20361.5 indexed citations
20.
Nayar, Shree K., et al.. (1990). Extracting Shape and Reflectance of Glossy Surfaces by Using 3D Photometric Sampling Method. Machine Vision and Applications. 133–136.1 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.