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Proceedings chapter
Open access
English

Real-time Scale-invariant Object Recognition from Light Field Imaging

Presented at Rome (Italy), 27-29 February 2016
PublisherSCITEPRESS - Science and Technology Publications
Publication date2016
Abstract

We present a novel light field dataset along with a real-time and scale-invariant object recognition system. Our method is based on bag-of-visual-words and codebook approaches. Its evaluation was carried out on a subset of our dataset of unconventional images. We show that the low variance in scale inferred from the specificities of a plenoptic camera allows high recognition performance. With one training image per object to recognise, recognition rates greater than 90 % are demonstrated despite a scale variation of up to 178 %. Our versatile light-field image dataset, CSEM-25, is composed of five classes of five instances captured with the recent industrial Raytrix R5 camera at different distances with several poses and backgrounds. We make it available for research purposes.

Keywords
  • Object Recognition
  • Object Classification
  • Light Field
  • Plenoptic Function
  • Scale Invariance
  • Real-time
  • Dataset
Citation (ISO format)
CLOIX, Séverine, PUN, Thierry, HASLER, David. Real-time Scale-invariant Object Recognition from Light Field Imaging. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2016. Rome (Italy). [s.l.] : SCITEPRESS - Science and Technology Publications, 2016. p. 336–344. doi: 10.5220/0005678603360344
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Proceedings chapter (Published version)
Identifiers
ISBN978-989-758-175-5
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123downloads

Technical informations

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