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Proceedings Paper

Computationally efficient background subtraction in the light field domain
Author(s): Alireza Ghasemi; Mahdad Hosseini Kamal; Martin Vetterli
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Paper Abstract

In this paperwe present a novel approach for depth estimation and background subtraction in light field images. our approach exploits the regularity and the internal structure of the light field signal in order to extract an initial depth map of the captured scene and uses the extracted depth map as the input to a final segmentation algorithm which finely isolates the background in the image. Background subtraction is natural application of the light field information since it is highly involved with depth information and segmentation. However many of the approaches proposed so far are not optimized specifically for background subtraction and are highly computationally expensive. Here we propose an approach based on a modified version of the well-known Radon Transform and not involving massive matrix calculations. It is therefore computationally very efficient and appropriate for real-time use. Our approach exploits the structured nature of the light field signal and the information inherent in the plenoptic space in order to extract an initial depth map and background model of the captured scene. We apply a modified the Radon transform and the gradient operator to horizontal slices of the light field signal to infer the initial depth map. The initial depth estimated are further refined to a precise background using a series of depth thresholding and segmentation in ambiguous areas. We test on method on various types real and synthetic of light field images. Scenes with different levels of clutter and also various foreground object depth have been considered in the experiments. The results of our experiments show much better computational complexity while retaining comparable performance to similar more complex methods.

Paper Details

Date Published: 7 March 2014
PDF: 8 pages
Proc. SPIE 9020, Computational Imaging XII, 90200U (7 March 2014); doi: 10.1117/12.2041161
Show Author Affiliations
Alireza Ghasemi, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Mahdad Hosseini Kamal, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Martin Vetterli, Ecole Polytechnique Fédérale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)

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