THREE-STEP REGISTRATION AND MULTI-THREAD PROCESSING BASED IMAGE MOSAIC FOR UNMANNED AERIAL VEHICLE APPLICATIONS

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

3
Reader(s)
7
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 9 , ISSUE 2 (June 2016) > List of articles

THREE-STEP REGISTRATION AND MULTI-THREAD PROCESSING BASED IMAGE MOSAIC FOR UNMANNED AERIAL VEHICLE APPLICATIONS

Hongguang Li * / Wenrui Ding / Yufeng wang

Keywords : UAV, image mosaic, image registration, multi-thread processing.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 2, Pages 1,090-1,109, DOI: https://doi.org/10.21307/ijssis-2017-909

License : (CC BY-NC-ND 4.0)

Received Date : 30-November-2015 / Accepted: 02-April-2016 / Published Online: 01-June-2016

ARTICLE

ABSTRACT

In the area of image mosaic for unmanned aerial vehicle (UAV) applications, the problems of precision and time consumption have drawn many scholars’ attention. To address above two problems, a novel algorithm based on three-step registration and multi-thread processing is proposed in this paper. This method divides the image registration into three steps to improve the precision. Firstly, based on the SIFT features, the fast index mechanism k-d tree and the Euclidean distance are utilized to determine the common points between two adjacent images; then, the linear slope constraint model is used to filter the mismatching point-pairs; finally, the RANSAC algorithm is adopted to remove outer points from the common points to ensure matching precision of inter frames. To accommodate the real time requirement of UAV application, a parallel data processing pattern is presented. The multi-core resources and multi-thread computing method of computers are employed adequately in the new pattern to speed up the whole algorithm. Extensive experiments on precision and time consumption show the superior performance of the proposed algorithm.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] Niethammer U, James M R, Rothmund S, et al., “UAV-based remote sensing of the Super-
Sauze landslide: Evaluation and results”, Engineering Geology, 2012, 128(11):2-11.
[2] Yanmin L, Peizhong L, et al., “An artificial immune network clustering algorithm for
mangroves remote sensing image”, International Journal on Smart Sensing & Intelligent Systems,
2014, 7(1): 116 – 134.
[3] Feng Q, Liu J, Gong J., “Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote
Sensing and Random Forest Classifier—A Case of Yuyao, China”, Water, 2015, 7(4):1437-
1455.
[4] Watts A C, Ambrosia V G, Hinkley E A., “Unmanned Aircraft Systems in Remote Sensing
and Scientific Research: Classification and Considerations of Use”, Remote Sensing, 2012,
4(6):1671-1692.
[5] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints// International Journal
of Computer Vision. 2004:91-110.
[6] Brown M, Lowe D G. Automatic panoramic image stitching using invariant features//
International Journal of Computer Vision. 2007:59-73.
[7] Abdel-Hakim A E, Farag A. CSIFT: A SIFT Descriptor with Color Invariant Characteristics//
IEEE Computer Society Conference on Computer Vision & Pattern Recognition (CVPR),
2006:1978-1983.
[8] Zeng L, Zhang S, Zhang J, et al., “Dynamic image mosaic via SIFT and dynamic
programming”, Machine Vision & Applications, 2014, 25(5):1271-1282.
[9] Bay H, Ess A, Tuytelaars T, et al., “Speeded-Up Robust Features (SURF)[J]. Computer
Vision & Image Understanding,”, 2008, 110(3):346-359.
[10] Rublee E, Rabaud V, Konolige K, et al., “ORB: An efficient alternative to SIFT or SURF”,
Proceedings, 2011, 58(11):2564-2571.
[11] Yang Y, Sun G, Zhao D, et al., “A Real Time Mosaic Method for Remote Sensing Video
Images from UAV “, Journal of Signal & Information Processing, 2013, 04(3B):168-172.
[12] Haripriya Y, Bindu Pavani K V, Lavanya S, et al., “Feature Based Image Stitching on Aerial
Images”, International Journal of Applied Engineering Research, 2013.
[13] Wang H, Li J, Wang L, et al., “Automated mosaicking of UAV images based on SFM
method”, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
IEEE, 2014:2633 - 2636.
[14] Zhouping Y., “Fusion algorithm of optical images and SAR with SVT and sparse
representation”, International Journal on Smart Sensing & Intelligent Systems, 2015, 8(2):1123-
1141.
[15] Jansson J, Gustafsson F., “Image Stitching Using Structure Deformation”, IEEE
Transactions on Pattern Analysis & Machine Intelligence, 2008, 30(4):617-631.
[16] Chandratre R, A Chakkarwar V., “Image Stitching using Harris and RANSAC”,
International Journal of Computer Applications, 2014, 89(15):14-19.
[17] Yongjin Y, Xinmei Z, et al., “Research of Image Pre-processing Algorithm Based on
FPGA”, International Journal on Smart Sensing & Intelligent Systems, 2013, 6(4): 1499 – 1515.
[18] Nistér D. Preemptive RANSAC for live structure and motion estimation, Computer Vision,
2003. Proceedings, Ninth IEEE International Conference on. IEEE, 2003:199-206 vol.1.
[19] Beis J S, Lowe D G., “Shape Indexing Using Approximate Nearest-Neighbour Search in
High-Dimensional Spaces”, Proceedings of the 1997 Conference on Computer Vision and Pattern
Recognition (CVPR '97). IEEE Computer Society, 1997:1000.
[20] Lourakis M I A., “A Brief Description of the Levenberg-Marquardt Algorithm Implemened
by levmar”, Foundation of Research & Technology, 2005.

EXTRA FILES

COMMENTS