Uncertainty preserving patch-based online modeling for 3D model acquisition and integration from passive motion imagery

Hao Tang; Peng Chang; Edgardo Molina; Zhigang Zhu

doi:10.1117/12.918790

3 May 2012 Uncertainty preserving patch-based online modeling for 3D model acquisition and integration from passive motion imagery

Hao Tang, Peng Chang, Edgardo Molina, Zhigang Zhu

Author Affiliations +

Proceedings Volume 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI; 840203 (2012) https://doi.org/10.1117/12.918790
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Hao Tang, Peng Chang, Edgardo Molina, and Zhigang Zhu "Uncertainty preserving patch-based online modeling for 3D model acquisition and integration from passive motion imagery", Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 840203 (3 May 2012); https://doi.org/10.1117/12.918790

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

3D modeling

Data modeling

3D image processing

Motion models

Atomic force microscopy

Cameras

Clouds

Show All Keywords

Keywords/Phrases

Search In:

Publication Years