4D light field segmentation with spatial and angular consistencies

Hajime Mihara, Takuya Funatomi, Kenichiro Tanaka, Hiroyuki Kubo, Yasuhiro Mukaigawa, Hajime Nagahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

In this paper, we describe a supervised four-dimensional (4D) light field segmentation method that uses a graph-cut algorithm. Since 4D light field data has implicit depth information and contains redundancy, it differs from simple 4D hyper-volume. In order to preserve redundancy, we define two neighboring ray types (spatial and angular) in light field data. To obtain higher segmentation accuracy, we also design a learning-based likelihood, called objectness, which utilizes appearance and disparity cues. We show the effectiveness of our method via numerical evaluation and some light field editing applications using both synthetic and real-world light fields.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467386234
DOIs
Publication statusPublished - Jun 15 2016
Event2016 IEEE International Conference on Computational Photography, ICCP 2016 - Evanston, United States
Duration: May 13 2016May 15 2016

Publication series

Name2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings

Other

Other2016 IEEE International Conference on Computational Photography, ICCP 2016
Country/TerritoryUnited States
CityEvanston
Period5/13/165/15/16

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Instrumentation

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