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.
Deep learning and process understanding for data-driven Earth system science
20193.0k citationsMarkus Reichstein, Joachim Denzler et al.profile →
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
202460 citationsMarkus Reichstein, Joachim Denzler 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 Joachim Denzler
Since
Specialization
Citations
This map shows the geographic impact of Joachim Denzler'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 Joachim Denzler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joachim Denzler more than expected).
This network shows the impact of papers produced by Joachim Denzler. 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 Joachim Denzler. The network helps show where Joachim Denzler may publish in the future.
Co-authorship network of co-authors of Joachim Denzler
This figure shows the co-authorship network connecting the top 25 collaborators of Joachim Denzler.
A scholar is included among the top collaborators of Joachim Denzler 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 Joachim Denzler. Joachim Denzler is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kähler, Olaf & Joachim Denzler. (2008). Robust Real-Time SFM in a Combined Formulation of Tracking and Reconstruction.. Vision Modeling and Visualization. 283–292.1 indexed citations
11.
Denzler, Joachim, et al.. (2008). Challenging Anomaly Detection in Wire Ropes Using Linear Prediction Combined with One-class Classification.. Vision Modeling and Visualization. 343–352.3 indexed citations
12.
Butko, Nicholas J., et al.. (2006). A Comparison of Nearest Neighbor Search Algorithms for Generic Object Recognition.6 indexed citations
Rohlfing, Torsten, et al.. (2005). Adaptive performance-based classifier combination for generic object recognition.2 indexed citations
15.
Rohlfing, Torsten, et al.. (2004). Markerless Real-Time Target Region Tracking: Application to Frameless Sterotactic Radiosurgery.. Vision Modeling and Visualization. 5–12.1 indexed citations
16.
Denzler, Joachim, et al.. (2003). Plenoptic Models in Robot Vision.. Künstliche Intell.. 17. 63.2 indexed citations
17.
Denzler, Joachim, et al.. (2002). Generic Hierarchic Object Models and Classification Based on Probabilistic PCA.. Machine Vision and Applications. 435–438.3 indexed citations
18.
Denzler, Joachim, et al.. (2002). Calibration of Real Scenes for the Reconstruction of Dynamic Light Fields. IEICE Transactions on Information and Systems. 87(1). 32–35.
19.
Denzler, Joachim & Heinrich Niemann. (1996). 3D Data Driven Prediction for Active Contour Models with Application to Car Tracking. Machine Vision and Applications. 204–207.1 indexed citations
20.
Denzler, Joachim & H. Niemann. (1994). A two stage real-time object tracking system. Hrčak Portal of scientific journals of Croatia (University Computing Centre). 2(3). 211–221.5 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.