Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera

Ramon Soria, P; Sukkar, F; Martens, W; Arrue, BC; Fitch, R

Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera

Ramon Soria, P Sukkar, F

Martens, W Arrue, BC Fitch, R

Permalink

Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2018, 694 pp. 320 - 331
Issue Date:: 2018-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (4.99 MB)

Adobe PDF

Download Published versionAdobe PDF (1.17 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ramon Soria, P	en_US
dc.contributor.author	Sukkar, F https://orcid.org/0000-0002-7041-5151	en_US
dc.contributor.author	Martens, W	en_US
dc.contributor.author	Arrue, BC	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2018, 694 pp. 320 - 331	en_US
dc.identifier.isbn	9783319708355	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134265
dc.description.abstract	© Springer International Publishing AG 2018. Fruit harvesting is a topic of intereset in agricultural industries. In order to perform this task, robots should be able to recognise and segment fruit in their perceptual environment. Particularly, apple trees are often arranged as planar trellis structures in commercial orchards. The vine-like branches have leaves that can occlude fruit and produce noise in typical depth sensor data that also populates the scene with objects that are not of interest. In this paper, we present a method that uses a Dirichlet mixture of Gaussian processes and a Gibbs-Sampler for segmenting clusters of apples to support selective autonomous harvesting. Furthermore, the model provides probabilistic reconstruction of the entire apple which can be used for better grasping of the fruit.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-319-70836-2_27	en_US
dc.title	Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera	en_US
dc.type	Conference Proceeding
utslib.citation.volume	694	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	694	en_US

Abstract:

© Springer International Publishing AG 2018. Fruit harvesting is a topic of intereset in agricultural industries. In order to perform this task, robots should be able to recognise and segment fruit in their perceptual environment. Particularly, apple trees are often arranged as planar trellis structures in commercial orchards. The vine-like branches have leaves that can occlude fruit and produce noise in typical depth sensor data that also populates the scene with objects that are not of interest. In this paper, we present a method that uses a Dirichlet mixture of Gaussian processes and a Gibbs-Sampler for segmenting clusters of apples to support selective autonomous harvesting. Furthermore, the model provides probabilistic reconstruction of the entire apple which can be used for better grasping of the fruit.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/133273