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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2856

Title: 3D-object space reconstruction from planar recorded data
Authors: Monaleche Cirstea, S
Kung, SY
McCormick, M
Aggoun, A
Keywords: 3D Imaging
Inverse problems
Object space reconstruction
Regularisation methods
Publication Date: 2003
Publisher: Kluwer
Citation: Journal of VLSI Signal Processing. 35(1): 5–18
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.
Description: The published version of this article is accessible from the link below.
URI: http://bura.brunel.ac.uk/handle/2438/2856
http://www.springerlink.com/content/t885833m6kk56530/
DOI: http://dx.doi.org/10.1023/A:1023386402756
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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