Ian Stavness

3.8k total citations
111 papers, 2.1k citations indexed

About

Ian Stavness is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Plant Science. According to data from OpenAlex, Ian Stavness has authored 111 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 24 papers in Human-Computer Interaction and 23 papers in Plant Science. Recurrent topics in Ian Stavness's work include Smart Agriculture and AI (21 papers), Phonetics and Phonology Research (16 papers) and Interactive and Immersive Displays (15 papers). Ian Stavness is often cited by papers focused on Smart Agriculture and AI (21 papers), Phonetics and Phonology Research (16 papers) and Interactive and Immersive Displays (15 papers). Ian Stavness collaborates with scholars based in Canada, United States and Japan. Ian Stavness's co-authors include Jordan Ubbens, Sidney Fels, John E. Lloyd, Mikolaj Cieslak, Przemysław Prusinkiewicz, A.G. Hannam, Bryan Gick, Carl Gutwin, Eva Piehslinger and Martina Schmid‐Schwap and has published in prestigious journals such as Journal of Neurophysiology, Scientific Reports and The Journal of the Acoustical Society of America.

In The Last Decade

Ian Stavness

109 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ian Stavness Canada	23	632	321	318	262	196	111	2.1k
Ming Ronnier Luo United Kingdom	36	64 0.1×	49 0.2×	1.7k 5.5×	54 0.2×	15 0.1×	234	6.1k
Daniela Giordano Italy	27	36 0.1×	48 0.1×	703 2.2×	59 0.2×	51 0.3×	153	2.6k
Athanasios Papaioannou Greece	17	138 0.2×	49 0.2×	639 2.0×	32 0.1×	5 0.0×	54	1.4k
Xavier P. Burgos-Artizzu Spain	18	850 1.3×	510 1.6×	705 2.2×	54 0.2×		41	2.2k
H. K. Sardana India	19	54 0.1×	30 0.1×	307 1.0×	12 0.0×	30 0.2×	74	1.3k
Flavio Prieto Colombia	20	311 0.5×	123 0.4×	322 1.0×	80 0.3×		139	1.5k
Wencheng Wu United States	9	55 0.1×	22 0.1×	716 2.3×	20 0.1×	13 0.1×	36	1.7k
Hiroshi Kobayashi Japan	29	15 0.0×	48 0.1×	567 1.8×	152 0.6×	27 0.1×	199	2.7k
Carlos M. Travieso Spain	31	82 0.1×	121 0.4×	1.3k 4.2×	237 0.9×	1 0.0×	286	4.0k
Hiroshi Takemura Japan	16	134 0.2×	11 0.0×	153 0.5×	48 0.2×	11 0.1×	229	1.2k

Countries citing papers authored by Ian Stavness

Since Specialization

Citations

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

Fields of papers citing papers by Ian Stavness

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ian Stavness

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

All Works

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20 of 20 papers shown

Ubbens, Jordan, Ian Stavness, Michael P. Pound, & Wei Guo. (2025). Deep learning in plant phenotyping: the first ten years. Plant Phenomics. 7(4). 100062–100062.

Schmid‐Schwap, Martina, et al.. (2024). The effect of bolus properties on muscle activation patterns and TMJ loading during unilateral chewing. Journal of the mechanical behavior of biomedical materials. 151. 106401–106401. 4 indexed citations

Shirtliffe, Steven J., et al.. (2024). Counting Canola: Toward Generalizable Aerial Plant Detection Models. Plant Phenomics. 6. 268–268. 3 indexed citations

Eramian, Mark, et al.. (2023). Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns. Plant Phenomics. 5. 25–25. 11 indexed citations

Eramian, Mark, et al.. (2023). Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping. Plant Phenomics. 5. 37–37. 6 indexed citations

Sóskuthy, Márton, et al.. (2023). Postural adaptation to microgravity underlies fine motor impairment in astronauts’ speech. Scientific Reports. 13(1). 8231–8231. 3 indexed citations

David, Étienne, Daniel Smith, Scott Chapman, et al.. (2023). Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting. Plant Phenomics. 5. 59–59. 8 indexed citations

Ubbens, Jordan, Mitchell J. Feldmann, Ian Stavness, & Andrew Sharpe. (2022). Quantitative evaluation of nonlinear methods for population structure visualization and inference. G3 Genes Genomes Genetics. 12(9). 2 indexed citations

Mondal, Debajyoti, et al.. (2022). Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification. Frontiers in Artificial Intelligence. 5. 871162–871162. 8 indexed citations

10.

Schmid‐Schwap, Martina, et al.. (2021). Effect of facet inclination and location on TMJ loading during bruxism: An in-silico study. Journal of Advanced Research. 35. 25–32. 20 indexed citations

11.

David, Étienne, Simon Madec, Pouria Sadeghi‐Tehran, et al.. (2020). Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods. Plant Phenomics. 2020. 3521852–3521852. 168 indexed citations

12.

Stavness, Ian, et al.. (2018). Object Counting with Small Datasets of Large Images.. arXiv (Cornell University). 6 indexed citations

13.

Balm, Alfons J. M., et al.. (2017). Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model. International Journal of Computer Assisted Radiology and Surgery. 13(1). 47–59. 7 indexed citations

14.

Ubbens, Jordan & Ian Stavness. (2017). Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Frontiers in Plant Science. 8. 1190–1190. 236 indexed citations

15.

Anderson, Peter G., Sidney Fels, Ian Stavness, & Bryan Gick. (2016). Intrinsic and Extrinsic Portions of Soft Palate Muscles in Velopharyngeal and Oropharyngeal Constriction: A 3D Modeling Study. Canadian acoustics. 44(4). 18–19. 1 indexed citations

16.

Stavness, Ian, et al.. (2016). Does swallowing bootstrap speech learning. Canadian acoustics. 44(3). 2 indexed citations

17.

Flynn, Cormac, Ian Stavness, John E. Lloyd, & Sidney Fels. (2013). A finite element model of the face including an orthotropic skin model underin vivotension. Computer Methods in Biomechanics & Biomedical Engineering. 18(6). 571–582. 18 indexed citations

18.

Stavness, Ian, John E. Lloyd, & Sidney Fels. (2012). Automatic prediction of tongue muscle activations using a finite element model. Journal of Biomechanics. 45(16). 2841–2848. 69 indexed citations

19.

Gick, Bryan, et al.. (2011). Categorical variation in lip posture is determined by quantal biomechanical-articulatory relations. Canadian acoustics. 39(3). 178–179. 11 indexed citations

20.

Stavness, Ian, A.G. Hannam, John E. Lloyd, & Sidney Fels. (2008). Towards predicting biomechanical consequences of jaw reconstruction. PubMed. 2008. 4567–4570. 8 indexed citations

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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