Journal of VLSI signal processing systems for signal, image and video technology

, Volume 35, Issue 1, pp 5–18

3D-Object Space Reconstruction from Planar Recorded Data of 3D-Integral Images

Authors

  • Silvia Manolache Cirstea
    • Department of Electrical EngineeringPrinceton University
    • Department of Engineering and TechnologyDe Montfort University
  • S.Y. Kung
    • Department of Electrical EngineeringPrinceton University
  • Malcolm McCormick
    • Department of Engineering and TechnologyDe Montfort University
  • Amar Aggoun
    • Department of Engineering and TechnologyDe Montfort University
Article

DOI: 10.1023/A:1023386402756

Cite this article as:
Cirstea, S.M., Kung, S., McCormick, M. et al. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology (2003) 35: 5. doi:10.1023/A:1023386402756

Abstract

The paper presents a novel algorithm for object space reconstruction from the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by imposing additional conditions to the sought solution. An adaptive constrained 3D-reconstruction regularization algorithm based on the use of a sigmoid function is presented. A hierarchical multiresolution strategy which employes the adaptive constrained algorithm to obtain highly accurate intensity maps of the object space is described. The depth map of the object space is extracted from the intensity map using a weighted Durbin–Willshaw algorithm. Finally, illustrative simulation results are given.

integral imaging object space reconstruction inverse problems regularization methods gradient descent Durbin–Willshaw scheme

Copyright information

© Kluwer Academic Publishers 2003