Direct visual hazard affordance detection

(2019) Direct visual hazard affordance detection. PhD by Publication, Queensland University of Technology.

Description

This research investigates how robotic and autonomous perceptual systems can detect the action possibilities, or affordances, of objects in their environment. Specifically, hazard affordances are detected, as they are a type of detrimental action allowed by some objects. Trip hazard detection on construction sites is the primary, but not the only application domain of this direct visual affordance detection approach.

Impact and interest:

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

248 since deposited on 18 Jun 2019
83 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 129572
Item Type: QUT Thesis (PhD by Publication)
Supervisor: Milford, Michael & Suenderhauf, Niko
Keywords: Visual Affordance Detection, Affordance Detection, Affordances, Robotic Vision, Deep Learning, Computer Vision, Robotics
DOI: 10.5204/thesis.eprints.129572
Divisions: Past > QUT Faculties & Divisions > Science & Engineering Faculty
Past > Schools > School of Electrical Engineering & Computer Science
Institution: Queensland University of Technology
Deposited On: 18 Jun 2019 13:30
Last Modified: 17 Jan 2025 00:48