Michael Halstead

497 citations
21 papers · 360 · h-index 11

Impact in

Papers in

    • Video Surveillance and Tracking Methods 7
    • Advanced Image and Video Retrieval Techniques 6
    • Human Pose and Action Recognition 3
    • Face recognition and analysis 3
    • Advanced Neural Network Applications 3
    • Smart Agriculture and AI 11

Michael Halstead

21 papers receiving 350 citations

Peers

Michael Halstead
Comparison fields: 5 of 54
  • Computer Vision and Pattern Recognition 154
  • Plant Science 205
  • Analytical Chemistry 35
  • Environmental Engineering 36
  • Aerospace Engineering 46
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Arun N. Sivakumar United States
Caicong Wu China
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Taehyeong Kim South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Michael Halstead

Since Specialization
Citations

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

Fields of papers citing papers by Michael Halstead

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Michael Halstead, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Halstead Line = papers co-authored together Michael Halstead links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018102
2 202237
3 201427
4 202126
5 202225
6 201525
7 202219
8 202317
9 202017
10 201213
11
Tampered Speaker Inconsistency Detection with Phonetically Aware Audio-visual Features
201910
12 201810
13 20246
14 20226
15 20184
16 20244
17 20243
18 20173
19 20232
20 20152

About Michael Halstead

Michael Halstead is a scholar working on Computer Vision and Pattern Recognition, Plant Science, Aerospace Engineering, Artificial Intelligence and Ecology, having authored 21 papers that have together received 360 indexed citations. Recurring topics across this work include Smart Agriculture and AI (11 papers), Video Surveillance and Tracking Methods (7 papers), Advanced Image and Video Retrieval Techniques (6 papers), Robotics and Sensor-Based Localization (5 papers), Remote Sensing in Agriculture (3 papers), Human Pose and Action Recognition (3 papers), Face recognition and analysis (3 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (154 citations), Plant Science (205 citations), Analytical Chemistry (35 citations), Environmental Engineering (36 citations) and Aerospace Engineering (46 citations). Michael Halstead has collaborated with scholars based in Germany, Australia and United States. Frequent co-authors include Chris McCool, Simon Denman, Clinton Fookes, Alireza Ahmadi, Tristán Pérez, Sridha Sridharan, Thomas Läbe, Cyrill Stachniss, Oliver Schmittmann and Ignacio Vizzo. Their work appears in journals such as IEEE Robotics and Automation Letters, Journal of Visual Communication and Image Representation, Frontiers in Plant Science, The International Journal of Robotics Research and Pattern Recognition 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.

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