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.
"GrabCut"
20043.8k citationsCarsten Rother, Andrew Blake et al.profile →
Real-time human pose recognition in parts from single depth images
20112.4k citationsJamie Shotton, Andrew Blake et al.profile →
This map shows the geographic impact of Andrew Blake'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 Andrew Blake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Blake more than expected).
This network shows the impact of papers produced by Andrew Blake. 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 Andrew Blake. The network helps show where Andrew Blake may publish in the future.
Co-authorship network of co-authors of Andrew Blake
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Blake.
A scholar is included among the top collaborators of Andrew Blake 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 Andrew Blake. Andrew Blake is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Agarwal, Anant & Andrew Blake. (2009). Dense Stereo Matching over the Panum Band. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(3). 416–430.6 indexed citations
7.
Criminisi, Antonio, Geoffrey Cross, Andrew Blake, & Kolmogorov. (2006). Bilayer Segmentation of Live Video.. UCL Discovery (University College London).3 indexed citations
8.
Williams, Oliver, Andrew Blake, & Roberto Cipolla. (2005). Sparse Bayesian learning for efficient visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(8). 1292–1304.151 indexed citations
9.
Williams, Oliver, Andrew Blake, & Roberto Cipolla. (2004). The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data. Cambridge University Engineering Department Publications Database. 17. 1497–1504.6 indexed citations
Bishop, Chris, Andrew Blake, & Bhaskara Marthi. (2003). Super-resolution Enhancement of Video. International Conference on Artificial Intelligence and Statistics. 25–32.84 indexed citations
12.
Criminisi, Antonio, Jamie Shotton, Andrew Blake, & Philip H. S. Torr. (2003). Gaze Manipulation for One-to-one Teleconferencing. Oxford University Research Archive (ORA) (University of Oxford). 191–198.56 indexed citations
Curwen, R. & Andrew Blake. (1993). Dynamic contours: real-time active splines. MIT Press eBooks. 39–57.56 indexed citations
15.
Cipolla, Roberto & Andrew Blake. (1992). Motion planning using image divergence and deformation. Cambridge University Engineering Department Publications Database.5 indexed citations
16.
Zisserman, Andrew, Peter Giblin, & Andrew Blake. (1992). The information available to a moving observer from specularities. Oxford University Research Archive (ORA) (University of Oxford). 287–291.2 indexed citations
17.
Blake, Andrew, Roberto Cipolla, R. Curwen, Zhaoming Xie, & Andrew Zisserman. (1991). Visual guidance for robot motion. Cambridge University Engineering Department Publications Database.1 indexed citations
Blake, Andrew. (1984). Reconstructing a visible surface. National Conference on Artificial Intelligence. 23–26.22 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.