Sam Hallman
Impact in
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- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Image Retrieval and Classification Techniques
- Video Surveillance and Tracking Methods
- Visual Attention and Saliency Detection
- Advanced Vision and Imaging
Papers in
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- Advanced Image and Video Retrieval Techniques 3
- Advanced Neural Network Applications 2
- Video Surveillance and Tracking Methods 1
- Medical Image Segmentation Techniques 1
- Aging 2
- Genetics, Aging, and Longevity in Model Organisms 2
- Co-authors
- Charless C. Fowlkes (5 shared papers)Yi Yang (2 shared papers)Deva Ramanan (2 shared papers)Raúl Arteche Díaz (1 shared paper)Olivier Cinquin (2 shared papers)Adrian Paz (2 shared papers)Amanda Cinquin (2 shared papers)Michael Chiang (2 shared papers)
- Journals
- PLoS Genetics (1 paper)BMC Bioinformatics (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Sam Hallman
5 papers receiving 177 citations
Peers
Comparison fields: 5 of 44
- Aging 16
- Computer Vision and Pattern Recognition 143
- Media Technology 23
- Endocrine and Autonomic Systems 8
- Biophysics 6
Countries citing papers authored by Sam Hallman
This map shows the geographic impact of Sam Hallman'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 Sam Hallman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Hallman more than expected).
Fields of papers citing papers by Sam Hallman
This network shows the impact of papers produced by Sam Hallman. 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 Sam Hallman. The network helps show where Sam Hallman may publish in the future.
Co-authors
The 10 scholars most cited alongside Sam Hallman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 78 | |
| 2 | 2010 | 71 | |
| 3 | 2016 | 17 | |
| 4 | 2013 | 11 | |
| 5 | 2015 | 8 |
About Sam Hallman
Sam Hallman is a scholar working on Computer Vision and Pattern Recognition, Aging, Aerospace Engineering, Molecular Biology and Endocrine and Autonomic Systems, having authored 5 papers that have together received 185 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (3 papers), Robotics and Sensor-Based Localization (2 papers), Advanced Neural Network Applications (2 papers), Genetics, Aging, and Longevity in Model Organisms (2 papers), Single-cell and spatial transcriptomics (1 paper), Cell Image Analysis Techniques (1 paper), Video Surveillance and Tracking Methods (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Aging (16 citations), Computer Vision and Pattern Recognition (143 citations), Media Technology (23 citations), Endocrine and Autonomic Systems (8 citations) and Biophysics (6 citations). Sam Hallman has collaborated with scholars based in United States. Frequent co-authors include Charless C. Fowlkes, Yi Yang, Deva Ramanan, Raúl Arteche Díaz, Olivier Cinquin, Adrian Paz, Amanda Cinquin, Michael Chiang, Shimako Kawauchi and Anne L. Calof. Their work appears in journals such as PLoS Genetics, BMC Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.