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Proceedings Paper

Uncertainty preserving patch-based online modeling for 3D model acquisition and integration from passive motion imagery
Author(s): Hao Tang; Peng Chang; Edgardo Molina; Zhigang Zhu
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Paper Abstract

In both military and civilian applications, abundant data from diverse sources captured on airborne platforms are often available for a region attracting interest. Since the data often includes motion imagery streams collected from multiple platforms flying at different altitudes, with sensors of different field of views (FOVs), resolutions, frame rates and spectral bands, it is imperative that a cohesive site model encompassing all the information can be quickly built and presented to the analysts. In this paper, we propose to develop an Uncertainty Preserving Patch-based Online Modeling System (UPPOMS) leading towards the automatic creation and updating of a cohesive, geo-registered, uncertaintypreserving, efficient 3D site terrain model from passive imagery with varying field-of-views and phenomenologies. The proposed UPPOMS has the following technical thrusts that differentiate our approach from others: (1) An uncertaintypreserved, patch-based 3D model is generated, which enables the integration of images captured with a mixture of NFOV and WFOV and/or visible and infrared motion imagery sensors. (2) Patch-based stereo matching and multi-view 3D integration are utilized, which are suitable for scenes with many low texture regions, particularly in mid-wave infrared images. (3) In contrast to the conventional volumetric algorithms, whose computational and storage costs grow exponentially with the amount of input data and the scale of the scene, the proposed UPPOMS system employs an online algorithmic pipeline, and scales well to large amount of input data. Experimental results and discussions of future work will be provided.

Paper Details

Date Published: 4 May 2012
PDF: 11 pages
Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 840203 (4 May 2012); doi: 10.1117/12.918790
Show Author Affiliations
Hao Tang, The CUNY Graduate Ctr. (United States)
The City College of New York (United States)
Peng Chang, Princeton Vision LLC (United States)
Edgardo Molina, The CUNY Graduate Ctr. (United States)
The City College of New York (United States)
Zhigang Zhu, The City College of New York (United States)
The CUNY Graduate Ctr. (United States)


Published in SPIE Proceedings Vol. 8402:
Evolutionary and Bio-Inspired Computation: Theory and Applications VI
Olga Mendoza-Schrock; Mateen M. Rizki; Todd V. Rovito, Editor(s)

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