David Hall
David Hall
Verified email at qut.edu.au - Homepage
Cited by
Cited by
Evaluation of features for leaf classification in challenging conditions
D Hall, C McCool, F Dayoub, N Sunderhauf, B Upcroft
2015 IEEE Winter Conference on Applications of Computer Vision, 797-804, 2015
Probabilistic Object Detection: Definition and Evaluation
D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ...
arXiv preprint arXiv:1811.10800, 2018
Towards unsupervised weed scouting for agricultural robotics
D Hall, F Dayoub, J Kulk, C McCool
2017 IEEE International Conference on Robotics and Automation (ICRA), 5223-5230, 2017
A rapidly deployable classification system using visual data for the application of precision weed management
D Hall, F Dayoub, T Perez, C McCool
Computers and Electronics in Agriculture 148, 107-120, 2018
What can robotics research learn from computer vision research?
P Corke, F Dayoub, D Hall, J Skinner, N Sünderhauf
arXiv preprint arXiv:2001.02366, 2020
The Probabilistic Object Detection Challenge
J Skinner, D Hall, H Zhang, F Dayoub, N Sünderhauf
arXiv preprint arXiv:1903.07840, 2019
Benchmarking Sampling-based Probabilistic Object Detectors
D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
A transplantable system for weed classification by agricultural robotics
D Hall, F Dayoub, T Perez, C McCool
Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International …, 2017
A probabilistic challenge for object detection
N Sünderhauf, F Dayoub, D Hall, J Skinner, H Zhang, G Carneiro, ...
Nature Machine Intelligence 1 (9), 443-443, 2019
A rapidly deployable approach for automated visual weed classification without prior species knowledge
DR Hall
Queensland University of Technology, 2018
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