Proceedings chapter
Open access

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

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.

  • 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)

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