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.
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
20061.1k citationsJoão V. B. Soares, J. J. G. Leandro et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Michael J. Cree
Since
Specialization
Citations
This map shows the geographic impact of Michael J. Cree'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 Michael J. Cree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Cree more than expected).
This network shows the impact of papers produced by Michael J. Cree. 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 Michael J. Cree. The network helps show where Michael J. Cree may publish in the future.
Co-authorship network of co-authors of Michael J. Cree
This figure shows the co-authorship network connecting the top 25 collaborators of Michael J. Cree.
A scholar is included among the top collaborators of Michael J. Cree 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 Michael J. Cree. Michael J. Cree is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Frank, Eibe, et al.. (2018). Difference in details: transfer learning case study of "cryptic" plants and moths. Research Commons (University of Waikato).1 indexed citations
11.
Gouk, Henry, Bernhard Pfahringer, & Michael J. Cree. (2016). Learning Distance Metrics for Multi-Label Classification. Research Commons (University of Waikato). 63. 318–333.10 indexed citations
12.
Perrone, John A., et al.. (2014). Proceedings of the 29th International Conference on Image and Vision Computing New Zealand.1 indexed citations
Jelinek, Herbert F., et al.. (2005). Towards vessel characterisation in the vicinity of the optic disc in digital retinal images.30 indexed citations
15.
Soares, João–Bruno, et al.. (2005). USING THE 2-D MORLET WAVELET WITH SUPERVISED CLASSIFICATION FOR RETINAL VESSEL SEGMENTATION. Charles Sturt University Research Output (CRO). 0–0.12 indexed citations
16.
Cree, Michael J., David Cornforth, & Herbert F. Jelinek. (2005). Vessel segmentation and tracking using a two-dimensional model.21 indexed citations
17.
Cree, Michael J., Adrian A. Dorrington, & Dale A. Carnegie. (2005). A Heterodyning Range Imager. Machine Vision and Applications. 80–83.3 indexed citations
18.
Luckie, Alan, Herbert F. Jelinek, Michael J. Cree, et al.. (2004). Identification and follow–up of diabetic retinopathy in rural health in Australia: an automated screening model. Investigative Ophthalmology & Visual Science. 45(13). 5245–5245.4 indexed citations
19.
Cree, Michael J.. (1998). Preprocessing of Fluorescein Angiographic Retinal Images.. 67–72.1 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.