Mr. Ren Xiang Profile

Ren Xiang

at Peking Univ

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 December 2014 Paper

A nearly real-time UAV video flow mosaic method

H. Zheng, C. Jiang, M. Sun, X. Li, R. Xiang, Lei Liu

Proceedings Volume 9260, 926048 (2014) https://doi.org/10.1117/12.2069370

KEYWORDS: Video, Unmanned aerial vehicles, Video acceleration, Feature extraction, Video processing, Image processing, Sensors, Distortion, Cameras, Image resolution

Read Abstract +

In order to solve the problem of low accuracy and high computation cost of current video mosaic methods, and also to acquire large field of view images by the unmanned aerial vehicles (UAV), which have high accuracy and high resolution, this paper propose a method for near real-time mosaic of video flow, so that we can provide essential reference data for the earthquake relief, as well as post-disaster reconstruction and recovery, in time. In this method, we obtain the flight area scope in the route planning process, and calculate the sizes of each frame with sensor sizes and altitudes. Given an overlap degree, time intervals are calculated, and key frames are extracted. After that, feature points are detected in each frame, and they are matched using Hamming distance. The RANSAC algorithm is then applied to remove error matching and calculate parameters of the transformation model. In one-strip case, the newly extracted frame is taken as the reference image in the first half, while after the middle frame is extracted, it is the reference one until the end. Experimental results show that our method can reduce the cascading error, and improve the accuracy and quality of the mosaic images, near real-time mosaic of aerial video flow is feasible.

Proceedings Article | 8 November 2014 Paper

A method of fast mosaic for massive UAV images

Ren Xiang, Min Sun, Cheng Jiang, Lei Liu, Hui Zheng, Xiaodong Li

Proceedings Volume 9260, 92603W (2014) https://doi.org/10.1117/12.2069201

KEYWORDS: Unmanned aerial vehicles, Global Positioning System, Image processing, Data processing, Remote sensing, Cameras, Neodymium, Parallel computing, Control systems, Image resolution

Read Abstract +

With the development of UAV technology, UAVs are used widely in multiple fields such as agriculture, forest protection, mineral exploration, natural disaster management and surveillances of public security events. In contrast of traditional manned aerial remote sensing platforms, UAVs are cheaper and more flexible to use. So users can obtain massive image data with UAVs, but this requires a lot of time to process the image data, for example, Pix4UAV need approximately 10 hours to process 1000 images in a high performance PC. But disaster management and many other fields require quick respond which is hard to realize with massive image data. Aiming at improving the disadvantage of high time consumption and manual interaction, in this article a solution of fast UAV image stitching is raised. GPS and POS data are used to pre-process the original images from UAV, belts and relation between belts and images are recognized automatically by the program, in the same time useless images are picked out. This can boost the progress of finding match points between images. Levenberg-Marquard algorithm is improved so that parallel computing can be applied to shorten the time of global optimization notably. Besides traditional mosaic result, it can also generate superoverlay result for Google Earth, which can provide a fast and easy way to show the result data. In order to verify the feasibility of this method, a fast mosaic system of massive UAV images is developed, which is fully automated and no manual interaction is needed after original images and GPS data are provided. A test using 800 images of Kelan River in Xinjiang Province shows that this system can reduce 35%-50% time consumption in contrast of traditional methods, and increases respond speed of UAV image processing rapidly.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years