Maja Pokrić

428 total citations
24 papers, 264 citations indexed

About

Maja Pokrić is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Maja Pokrić has authored 24 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Maja Pokrić's work include Medical Image Segmentation Techniques (5 papers), Medical Imaging Techniques and Applications (3 papers) and Augmented Reality Applications (3 papers). Maja Pokrić is often cited by papers focused on Medical Image Segmentation Techniques (5 papers), Medical Imaging Techniques and Applications (3 papers) and Augmented Reality Applications (3 papers). Maja Pokrić collaborates with scholars based in Serbia, United Kingdom and Belgium. Maja Pokrić's co-authors include Boris Pokrić, N. A. Thacker, Dragan Kukolj, David C. Williamson, Vladimir Crnojević, Dubravko Ćulibrk, Vladimir Zlokolica, Paul A. Bromiley, Petar Knežević and Marc Leman and has published in prestigious journals such as IEEE Transactions on Image Processing, Computers in Human Behavior and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

Maja Pokrić

24 papers receiving 231 citations

Peers

Maja Pokrić
Comparison fields: 5 of 88
  • Computer Vision and Pattern Recognition 142
  • Human-Computer Interaction 32
  • Computer Networks and Communications 29
  • Radiology, Nuclear Medicine and Imaging 27
  • Cognitive Neuroscience 25
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Citations per field, relative to Maja Pokrić
Maja Pokrić · 1×
Citations per year, relative to Maja Pokrić
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Countries citing papers authored by Maja Pokrić

Since Specialization
Citations

This map shows the geographic impact of Maja Pokrić'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 Maja Pokrić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maja Pokrić more than expected).

Fields of papers citing papers by Maja Pokrić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maja Pokrić. 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 Maja Pokrić. The network helps show where Maja Pokrić may publish in the future.

Co-authorship network of co-authors of Maja Pokrić

This figure shows the co-authorship network connecting the top 25 collaborators of Maja Pokrić. A scholar is included among the top collaborators of Maja Pokrić 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 Maja Pokrić. Maja Pokrić is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 19
2 18
3 15
4 5
5 1
6 1
7 3
8
Comparison of algorithms for patent documents clusterization
5
9
PSALM - Tool for business intelligence
5
10 55
11 15
12 7
13 15
14
Bayesian and Non-Bayesian Probabilistic Models for Image Analysis
1
15 4
16 3
17
Partial Volume Tissue Segmentation using Grey-Level Gradient.
12
18
Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction
1
19
An integrated simulator for surgery of the petrous bone.
32
20 3

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.

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