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Representation Learning and Semantics for Robotic Vision

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thesis
posted on 13.08.2019 by Benjamin Joshua Meyer
Vision is a powerful tool in enabling intelligent robotic systems to safely and effectively perform useful tasks. An important precursor to well-considered robotic action is a semantic understanding of the environment, which can be facilitated by machine learning techniques. Recent advances in the field of machine learning for computer vision seldom make considerations for robotic applications. In this thesis, important problem domains for robotic vision are identified and explored. Machine learning methodologies are developed with motivation drawn from the needs of a robotic vision system.

History

Campus location

Australia

Principal supervisor

Thomas William Drummond

Additional supervisor 1

Lindsay Kleeman

Year of Award

2019

Department, School or Centre

Electrical and Computer Systems Engineering

Additional Institution or Organisation

ARC Centre of Excellence for Robotic Vision

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Engineering

Exports