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Detecting and Grouping Identical Objects for Region Proposal and Classification
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-6716-1111
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2017 (English)In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society, 2017, Vol. 2017, 501-502 p., 8014810Conference paper (Refereed)
Abstract [en]

Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify such objects. We use such an algorithm to provide object proposals to a convolutional neural network (CNN) based classifier. This results in fewer regions to evaluate, compared to traditional region proposal algorithms. Additionally, it enables using the joint probability of multiple instances of an object, resulting in improved classification accuracy. The proposed technique can also split a single class into multiple sub-classes corresponding to the different object types, enabling hierarchical classification.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017. Vol. 2017, 501-502 p., 8014810
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-218547DOI: 10.1109/CVPRW.2017.76Scopus ID: 2-s2.0-85030248255ISBN: 9781538607336 (print)OAI: oai:DiVA.org:kth-218547DiVA: diva2:1161444
Conference
30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, Honolulu, United States, 21 July 2017 through 26 July 2017
Note

QC 20171130

Available from: 2017-11-30 Created: 2017-11-30 Last updated: 2018-01-13Bibliographically approved

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Caccamo, Sergio

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf