Depth measurement from integral images through viewpoint image extraction and a modified multibaseline disparity analysis algorithm

Chun Hong Wu, Amar Aggoun, Malcolm McCormick, Sun-Yuan Kung

Research output: Contribution to journalReview article

6 Scopus citations

Abstract

Integral imaging is a technique capable of displaying images with continuous parallax in full natural color. This paper presents a method of extracting depth map from integral images through viewpoint image extraction. The approach starts with the constructions of special viewpoint images frem the integral image. Each viewpoint image contains a two-dimensional parallel recording of the three-dimensional scene. A new mathematical expression giving the relationship between object depth and the corresponding viewpoint image pair displacement is derived by geometrically analyzing the integral recording process. The depth can be calculated from the corresponding displacement between two viewpoint images. A modified multibaseline algorithm, where the baseline is defined as the sample distance between two viewpoint images, is further adopted to integrate the information from multiple extracted viewpoint images. The developed depth extraction method is validated and applied to both real photographic and computer generated unidirectional integral images. The depth measuring solution gives a precise description of the object thickness with an error of less than 0.3% from the photographic image in the example.

Original languageEnglish (US)
Article number023018
Pages (from-to)1-9
Number of pages9
JournalJournal of Electronic Imaging
Volume14
Issue number2
DOIs
StatePublished - Apr 1 2005

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Depth measurement from integral images through viewpoint image extraction and a modified multibaseline disparity analysis algorithm'. Together they form a unique fingerprint.

  • Cite this