Washington Mio

5.2k total citations · 1 hit paper
69 papers, 3.1k citations indexed

About

Washington Mio is a scholar working on Computer Vision and Pattern Recognition, Geometry and Topology and Computational Mechanics. According to data from OpenAlex, Washington Mio has authored 69 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 29 papers in Geometry and Topology and 13 papers in Computational Mechanics. Recurrent topics in Washington Mio's work include Morphological variations and asymmetry (23 papers), Medical Image Segmentation Techniques (11 papers) and Face and Expression Recognition (11 papers). Washington Mio is often cited by papers focused on Morphological variations and asymmetry (23 papers), Medical Image Segmentation Techniques (11 papers) and Face and Expression Recognition (11 papers). Washington Mio collaborates with scholars based in United States, Canada and Tanzania. Washington Mio's co-authors include Anuj Srivastava, S. K. Joshi, Xiuwen Liu, Eric Klassen, Shantanu H. Joshi, Mao Li, Benedikt Hallgrímsson, Qiuping Xu, Luke Mander and John Bryant and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, PLANT PHYSIOLOGY and Scientific Reports.

In The Last Decade

Washington Mio

66 papers receiving 2.9k citations

Hit Papers

Statistical shape analysis: clustering, learning, and tes... 2005 2026 2012 2019 2005 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Washington Mio United States 16 1.5k 987 380 359 322 69 3.1k
Charles R. Giardina United States 10 524 0.4× 688 0.7× 176 0.5× 242 0.7× 117 0.4× 44 3.0k
Ian L. Dryden United Kingdom 27 805 0.5× 399 0.4× 186 0.5× 122 0.3× 266 0.8× 109 2.4k
Anuj Srivastava United States 43 2.5k 1.7× 4.0k 4.1× 361 0.9× 351 1.0× 425 1.3× 250 8.5k
Frank P. Kuhl United States 8 534 0.4× 572 0.6× 174 0.5× 243 0.7× 106 0.3× 20 1.8k
Nina Amenta United States 26 226 0.2× 1.6k 1.6× 65 0.2× 170 0.5× 120 0.4× 74 4.5k
Massimiliano Corsini Italy 21 108 0.1× 1.4k 1.5× 50 0.1× 209 0.6× 70 0.2× 74 3.3k
Marco Callieri Italy 22 96 0.1× 1.0k 1.0× 48 0.1× 188 0.5× 83 0.3× 83 2.8k
Yaron Lipman Israel 36 198 0.1× 1.9k 2.0× 47 0.1× 128 0.4× 79 0.2× 74 4.4k
Matteo Dellepiane Italy 24 96 0.1× 1.1k 1.1× 48 0.1× 206 0.6× 58 0.2× 80 3.0k
Gérard Subsol France 19 140 0.1× 605 0.6× 62 0.2× 61 0.2× 135 0.4× 106 1.7k

Countries citing papers authored by Washington Mio

Since Specialization
Citations

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

Fields of papers citing papers by Washington Mio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Washington Mio

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

All Works

20 of 20 papers shown
1.
Mio, Washington, Ruiyue Chen, Yanqing Chen, et al.. (2025). Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation. Computerized Medical Imaging and Graphics. 123. 102543–102543.
2.
Mémoli, Facundo, et al.. (2019). A topological study of functional data and Fréchet functions of metric measure spaces. 3(4). 359–380. 3 indexed citations
3.
Mémoli, Facundo, et al.. (2018). The shape of data and probability measures. Applied and Computational Harmonic Analysis. 48(1). 149–181. 3 indexed citations
4.
Fan, Yu, et al.. (2018). Learning shape metrics with Monte Carlo optimization. Journal of Computational and Applied Mathematics. 348. 120–129. 2 indexed citations
5.
Mao, Li, Joanne B. Cole, Mange Manyama, et al.. (2017). Rapid automated landmarking for morphometric analysis of three‐dimensional facial scans. Journal of Anatomy. 230(4). 607–618. 26 indexed citations
6.
Jia, Dongyu, Qiuping Xu, Qian Xie, Washington Mio, & Wu‐Min Deng. (2016). Automatic stage identification of Drosophila egg chamber based on DAPI images. Scientific Reports. 6(1). 18850–18850. 65 indexed citations
7.
Cole, Joanne B., Mange Manyama, Jacinda R. Larson, et al.. (2016). Genomewide Association Study of African Children Identifies Association of SCHIP1 and PDE8A with Facial Size and Shape. PLoS Genetics. 12(8). e1006174–e1006174. 57 indexed citations
8.
Hallgrímsson, Benedikt, Christopher J. Percival, Rebecca M. Green, et al.. (2015). Morphometrics, 3D Imaging, and Craniofacial Development. Current topics in developmental biology. 115. 561–597. 62 indexed citations
9.
Xu, Qiuping, Heather A. Jamniczky, Diane Hu, et al.. (2015). Correlations Between the Morphology of Sonic Hedgehog Expression Domains and Embryonic Craniofacial Shape. Evolutionary Biology. 42(3). 379–386. 20 indexed citations
10.
Mander, Luke, Mao Li, Washington Mio, Charless C. Fowlkes, & Surangi W. Punyasena. (2013). Classification of grass pollen through the quantitative analysis of surface ornamentation and texture. Proceedings of the Royal Society B Biological Sciences. 280(1770). 20131905–20131905. 71 indexed citations
11.
Liu, Xiuwen, Yonggang Shi, Ivo D. Dinov, & Washington Mio. (2010). A Computational Model of Multidimensional Shape. International Journal of Computer Vision. 89(1). 69–83. 10 indexed citations
12.
Mio, Washington, Yonggang Shi, Ivo D. Dinov, et al.. (2008). Models of Normal Variation and Local Contrasts in Hippocampal Anatomy. Lecture notes in computer science. 11(Pt 2). 407–415. 10 indexed citations
13.
Wu, Yiming, Xiuwen Liu, & Washington Mio. (2008). Learning representations for object classification using multi-stage optimal component analysis. Neural Networks. 21(2-3). 214–221. 2 indexed citations
14.
Liu, Xiuwen, et al.. (2007). Content-Based Image Categorization and Retrieval using Neural Networks. 528–531. 5 indexed citations
15.
Mio, Washington, Anuj Srivastava, & Xiuwen Liu. (2006). Contour Inferences for Image Understanding. International Journal of Computer Vision. 69(1). 137–144. 2 indexed citations
16.
Joshi, Shantanu H., Anuj Srivastava, & Washington Mio. (2005). Elastic Shape Models for Interpolations of Curves in Image Sequences. Lecture notes in computer science. 541–552. 2 indexed citations
17.
Klassen, Eric, Anuj Srivastava, Washington Mio, & S. K. Joshi. (2004). Analysis of planar shapes using geodesic paths on shape spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(3). 372–383. 287 indexed citations
18.
Bryant, John & Washington Mio. (1999). EMBEDDINGS OF HOMOLOGY MANIFOLDS IN CODIMENSION⩾3. Topology. 38(4). 811–821. 4 indexed citations
19.
Mio, Washington. (1989). Nonlinearly equivalent representations of quaternionic 2-groups. Transactions of the American Mathematical Society. 315(1). 305–321. 1 indexed citations
20.
Mio, Washington. (1987). On boundary-link cobordism. Mathematical Proceedings of the Cambridge Philosophical Society. 101(2). 259–266. 21 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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