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Jake Fountain

dblp:143/7433 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
tracking
0.212016
Automatic identification of rigidly linked 6DoF sensors · VR 2016

Methods — techniques the papers use, named apart from their topics

simulation · 0.2sensor fusion · 0.2
YearPublicationVenuePosition
2019 Optimization of Robot Movements Using Genetic Algorithms and Simulation
Brandon Zahn, Jake Fountain, Trent Houliston, Alex Biddulph, Stephan K. Chalup, Alexandre Mendes
RoboCup2
2017 Training Deep Neural Networks for Detecting Drinking Glasses Using Synthetic Images
Luke Farrawell, Jake Fountain, Stephan K. Chalup
ICONIP (2)3
2016 Automatic identification of rigidly linked 6DoF sensors
abstract
We present techniques for automatically identifying relationships between rigidly linked 6DoF and 3DoF sensors belonging to different sensor systems. The techniques allow for subsequent automatic alignment of the sensor systems, increasing the usability of modular sensor systems. Two techniques are presented and analysed in simulation and a case study for performance under varying noise and latency conditions. Good results were achieved, with each sensor identified correctly in at least 60% of estimates, or 4 times greater than random selection. After a sample collection period of around 5 seconds, the matching is performed in less than 5ms and is scalable to noisier systems by using more samples. Our methods represent a key step in creating highly accessible modular multi-device 3D systems.
Jake Fountain, Shamus P. Smith
VR1
2013 Motivated Reinforcement Learning for Improved Head Actuation of Humanoid Robots
Jake Fountain, Josiah Walker, David M. Budden, Alexandre Mendes, Stephan K. Chalup
RoboCup1