Photometric Estimation

Katsushi Ikeuchi, Yasuyuki Matsushita, Ryusuke Sagawa, Hiroshi Kawasaki, Yasuhiro Mukaigawa, Ryo Furukawa, Daisuke Miyazaki

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Previous chapters considered methods of estimating geometric shape information by using active-lighting methods, which provide photometric characteristics of the target scene and light sources. Because BRDF depends on materials, adequate lighting and analysis provides rich information about those materials. Moreover, multispectral light gives us more robust identification capability. Furthermore, polarization and fluorescence are useful for material classification. Analyzing captured images enables us to separate an image into several photometric components. Additionally, a scene illuminated by multiple lights can be decomposed into an image illuminated by a single light. As such, active lighting is useful, not only for geometrical analysis, but also for photometrical analysis.

Original languageEnglish
Title of host publicationAdvances in Computer Vision and Pattern Recognition
PublisherSpringer Science and Business Media Deutschland GmbH
Pages183-209
Number of pages27
DOIs
Publication statusPublished - 2020

Publication series

NameAdvances in Computer Vision and Pattern Recognition
ISSN (Print)2191-6586
ISSN (Electronic)2191-6594

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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