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Detecting and Group...
Detecting and Grouping Identical Objects for Region Proposal and Classification
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Abbeloos, W. (författare)
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- Caccamo, Sergio (författare)
- KTH,Robotik, perception och lärande, RPL
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Ataer-Cansizoglu, E. (författare)
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Taguchi, Y. (författare)
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Feng, C. (författare)
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Lee, T. -Y (författare)
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(creator_code:org_t)
- IEEE Computer Society, 2017
- 2017
- Engelska.
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Ingår i: 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. - : IEEE Computer Society. - 9781538607336 ; , s. 501-502
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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