Stephen M. Omohundro

2.2k citations
25 papers · 1.0k indexed · h-index 13

Stephen M. Omohundro

24 papers receiving 916 citations

Peers

Stephen M. Omohundro
Comparison fields: 5 of 110
  • Signal Processing 234
  • Computer Vision and Pattern Recognition 394
  • Artificial Intelligence 504
  • Computer Graphics and Computer-Aided Design 33
  • Computational Theory and Mathematics 93
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Tibério S. Caetano Australia
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Citations per year

Countries citing papers authored by Stephen M. Omohundro

Since Specialization
Citations

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

Fields of papers citing papers by Stephen M. Omohundro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen M. Omohundro. 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 Stephen M. Omohundro. The network helps show where Stephen M. Omohundro may publish in the future.

Co-authorship network

The 8 scholars most cited alongside Stephen M. Omohundro, 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 Stephen M. Omohundro Line = papers co-authored together Stephen M. Omohundro links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Equilateral Triangles: A Challenge for Connectionist Vision
20090
2
Five Balltree Construction Algorithms
2009266
3 200212
4 199627
5
Family Discovery
199511
6
Nonlinear Image Interpolation using Manifold Learning
199466
7
Surface Learning with Applications to Lipreading
199365
8
The Sather programming language
19938
9
Hidden Markov Model Induction by Bayesian Model Merging
1992154
10 19921
11
Geometric learning algorithms
19917
12
Best-First Model Merging for Dynamic Learning and Recognition
199129
13
Bumptrees for Efficient Function, Constraint and Classification Learning
199051
14
How Receptive Field Parameters Affect Neural Learning
199010
15
A Network for Extracting the Locations of Point Clusters Using Selective Attention 1
19901
16 199024
17
Efficient Algorithms with Neural Network Behavior.
1987112
18 198620
19 198451
20 19842

About Stephen M. Omohundro

Stephen M. Omohundro is a scholar working on Signal Processing, Human-Computer Interaction and Biophysics, having authored 25 papers that have together received 1.0k indexed citations. Recurring topics across this work include Speech and Audio Processing (4 papers), Neural Networks and Applications (4 papers), Indoor and Outdoor Localization Technologies (3 papers), Visual perception and processing mechanisms (3 papers), Neural dynamics and brain function (2 papers), Visual Attention and Saliency Detection (2 papers), Gaze Tracking and Assistive Technology (2 papers) and Software Engineering Research (2 papers). The work is most often cited by research in Signal Processing (234 citations), Computer Vision and Pattern Recognition (394 citations) and Artificial Intelligence (504 citations). Stephen M. Omohundro has collaborated with scholars based in United States. Frequent co-authors include Christoph Bregler, Andreas Stolcke, John D. Crawford, Clemens Szyperski, David Stoutamire, Bartlett W. Mel, Yochai Konig and Subutai Ahmad. Their work appears in journals such as Physica D Nonlinear Phenomena, ACM Transactions on Programming Languages and Systems, Mathematical Biosciences, Computers in Physics and Complex Systems.

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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