Maja Krivokuća
- Computer Vision and Pattern Recognition top 2%
- Computational Mechanics top 2%
- Computer Graphics and Computer-Aided Design top 0.5%
- Environmental Engineering top 10%
- Signal Processing top 10%
- Co-authors
- Philip A. ChouRobert CohenSébastien LasserreJoan LlachVittorio BaronciniSebastian SchwarzErnestasia SiahaanRufael Mekuria
- Topics
- 3D Shape Modeling and Analysis (9 papers)Computer Graphics and Visualization Techniques (9 papers)Advanced Vision and Imaging (8 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionComputational Mechanics
- Journals
- IEEE Transactions on Image ProcessingIEEE Transactions on MultimediaIEEE Signal Processing Letters
- Partner nations
- United StatesFranceNew Zealand
In The Last Decade
Maja Krivokuća
12 papers receiving 652 citations
Hit Papers
Peers
Comparison fields: 5 of 32
- Computer Vision and Pattern Recognition 496
- Computational Mechanics 403
- Computer Graphics and Computer-Aided Design 372
- Environmental Engineering 114
- Signal Processing 96
Countries citing papers authored by Maja Krivokuća
This map shows the geographic impact of Maja Krivokuća'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 Krivokuća with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maja Krivokuća more than expected).
Fields of papers citing papers by Maja Krivokuća
This network shows the impact of papers produced by Maja Krivokuća. 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 Krivokuća. The network helps show where Maja Krivokuća may publish in the future.
Co-authorship network of co-authors of Maja Krivokuća
This figure shows the co-authorship network connecting the top 25 collaborators of Maja Krivokuća. A scholar is included among the top collaborators of Maja Krivokuća 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 Krivokuća. Maja Krivokuća is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 13 | |
| 4 | 7 | |
| 5 | 6 | |
| 6 | 49 | |
| 7 | 12 | |
| 8 | 51 | |
| 9 | Emerging MPEG Standards for Point Cloud Compressionbreakdown → | 509 |
| 10 | 2 | |
| 11 | 10 | |
| 12 | 1 | |
| 13 | 4 | |
| 14 | 4 |
About Maja Krivokuća
Maja Krivokuća is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 14 papers that have together received 669 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (9 papers), Computer Graphics and Visualization Techniques (9 papers) and Advanced Vision and Imaging (8 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (372 citations), Computer Vision and Pattern Recognition (496 citations) and Computational Mechanics (403 citations). Maja Krivokuća has collaborated with scholars based in United States, France and New Zealand. Frequent co-authors include Philip A. Chou, Robert Cohen, Sébastien Lasserre, Joan Llach, Vittorio Baroncini, Sebastian Schwarz, Ernestasia Siahaan, Rufael Mekuria, Marius Preda and Zhu Li. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Transactions on Multimedia and IEEE Signal Processing Letters.
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.