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.
The Watershed Transform: Definitions, Algorithms and Parallelization Strategies
2000904 citationsJos B. T. M. Roerdink, Arnold MeijsterFundamenta Informaticaeprofile →
Citations per year, relative to Arnold Meijster Arnold Meijster (= 1×)
peers
N. Sarkar
Countries citing papers authored by Arnold Meijster
Since
Specialization
Citations
This map shows the geographic impact of Arnold Meijster'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 Arnold Meijster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arnold Meijster more than expected).
This network shows the impact of papers produced by Arnold Meijster. 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 Arnold Meijster. The network helps show where Arnold Meijster may publish in the future.
Co-authorship network of co-authors of Arnold Meijster
This figure shows the co-authorship network connecting the top 25 collaborators of Arnold Meijster.
A scholar is included among the top collaborators of Arnold Meijster 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 Arnold Meijster. Arnold Meijster is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wiering, Marco, et al.. (2013). Deep Support Vector Machines for Regression Problems. University of Groningen research database (University of Groningen / Centre for Information Technology). 53–54.15 indexed citations
3.
Wiering, Marco, et al.. (2013). The Neural Support Vector Machine. University of Groningen research database (University of Groningen / Centre for Information Technology).10 indexed citations
Meijster, Arnold. (2004). Efficient sequential and parallel algorithms for morphological image processing. University of Groningen research database (University of Groningen / Centre for Information Technology).7 indexed citations
Meijster, Arnold, Michel A. Westenberg, & Michael H. F. Wilkinson. (2002). Interactive Shape Preserving Filtering and Visualization of Volumetric Data. University of Groningen research database (University of Groningen / Centre for Information Technology). 640–643.7 indexed citations
Roerdink, Jos B. T. M. & Arnold Meijster. (2000). The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae. 41(1,2). 187–228.904 indexed citations breakdown →
11.
Meijster, Arnold & Jos B. T. M. Roerdink. (1998). A Disjoint Set Algorithm For The Watershed Transform. University of Groningen research database (University of Groningen / Centre for Information Technology). 1665–1668.22 indexed citations
12.
Meijster, Arnold & Jos B. T. M. Roerdink. (1995). The Implementation of a Parallel Watershed Algorithm. Data Archiving and Networked Services (DANS).4 indexed citations
13.
Meijster, Arnold & Jos B. T. M. Roerdink. (1995). An Alternative Algorithm for Computing Watersheds on Shared Memory Parallel Computers. University of Groningen research database (University of Groningen / Centre for Information Technology).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.