NDLI: Adaptive depth map estimation from 3D integral image

Content Provider	IEEE Xplore Digital Library
Author	Alazawi, E. Aggoun, A. Abbod, M. Fatah, O.A. Swash, M.R.
Copyright Year	2013
Description	Author affiliation: Electron. & Comput. Eng., Brunel Univ., Uxbridge, UK (Alazawi, E.; Aggoun, A.; Abbod, M.; Fatah, O.A.; Swash, M.R.)
Abstract	Integral Imaging (InIm) is one of the most promising technologies for producing full color 3-D images with full parallax. InIm requires only one recording in obtaining 3D information and therefore no calibration is necessary to acquire depth values. The compactness of using InIm in depth measurement has been attracting attention as a novel depth extraction technique. In this paper, an algorithm for depth extraction that builds on previous work by the authors is presented. Three main problems in depth map estimation from InIm have been solved; the uncertainty and region homogeneity at image location where errors commonly appear in disparity process, dissimilar displacements within the matching block around object borders, object segmentation. This method is based on the distribution of the sample variance in sub-dividing non-overlapping blocks. A descriptor which is unique and distinctive for each feature on InIm has been achieved. Comparing to state-of-the-art techniques, it is shown that the proposed algorithm has improvements on two aspects: depth map extraction level, computational complexity.
Starting Page	1
Ending Page	6
File Size	696800
Page Count	6
File Format	PDF
ISBN	9781467360470
ISSN	21555044
DOI	10.1109/BMSB.2013.6621736
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-05
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis 3D Integral image Estimation k-NN majority vote Microoptics Automatic threshold Three-dimensional displays Disparity depth map Feature based matching Imaging Feature extraction Lenses Viewpoints images
Content Type	Text
Resource Type	Article

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