Marko Mihajlović
- Computer Vision and Pattern Recognition top 10%
- Computational Mechanics top 10%
- Computer Graphics and Computer-Aided Design top 5%
- Control and Systems Engineering
- Artificial Intelligence
- Co-authors
- Siyu TangYan ZhangMichael J. BlackShaofei WangAndreas GeigerMobyen Uddin AhmedAayush BansalMichael Zollhoefer
- Topics
- 3D Shape Modeling and Analysis (5 papers)Computer Graphics and Visualization Techniques (3 papers)Traffic Prediction and Management Techniques (2 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionComputational Mechanics
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Open MIND
- Partner nations
- SwitzerlandSerbiaGermany
In The Last Decade
Marko Mihajlović
9 papers receiving 192 citations
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 139
- Computational Mechanics 115
- Computer Graphics and Computer-Aided Design 61
- Control and Systems Engineering 46
- Artificial Intelligence 24
Countries citing papers authored by Marko Mihajlović
This map shows the geographic impact of Marko Mihajlović'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 Marko Mihajlović with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marko Mihajlović more than expected).
Fields of papers citing papers by Marko Mihajlović
This network shows the impact of papers produced by Marko Mihajlović. 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 Marko Mihajlović. The network helps show where Marko Mihajlović may publish in the future.
Co-authorship network of co-authors of Marko Mihajlović
This figure shows the co-authorship network connecting the top 25 collaborators of Marko Mihajlović. A scholar is included among the top collaborators of Marko Mihajlović 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 Marko Mihajlović. Marko Mihajlović 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 | 41 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 31 | |
| 7 | 77 | |
| 8 | A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS | 21 |
| 9 | A Machine Learning Approach to Classify Pedestrians’ Events based on IMU and GPS | 26 |
| 10 | 4 |
About Marko Mihajlović
Marko Mihajlović is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 10 papers that have together received 205 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (5 papers), Computer Graphics and Visualization Techniques (3 papers) and Traffic Prediction and Management Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (61 citations), Computer Vision and Pattern Recognition (139 citations) and Computational Mechanics (115 citations). Marko Mihajlović has collaborated with scholars based in Switzerland, Serbia and Germany. Frequent co-authors include Siyu Tang, Yan Zhang, Michael J. Black, Shaofei Wang, Andreas Geiger, Mobyen Uddin Ahmed, Aayush Bansal, Michael Zollhoefer, Shunsuke Saito and Nikola Popović. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Open MIND.
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.