Feature-based background registration in wide-area motion imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 8402, Issue , 2012, Pages

  Wu, Yi ^a,b   Chen, Genshe ^c   Blasch, Erik ^d   Bai, Li ^e   Ling, Haibin ^a  

^a Temple University   (United States)

^b NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c I Fusion Technologies Inc   (United States)

^d AIR FORCE RESEARCH LABORATORY   (United States)

^e Temple University   (United States)

Author keywords

image registration; kd tree; RANSAC; Speeded Up Robust Feature (SURF); wide area motion imagery (WAMI)

Indexed keywords

CRITICAL PROBLEMS; KD-TREE; RANDOM SAMPLE CONSENSUS (RANSAC) ALGORITHM; RANSAC; SITUATION AWARENESS; SPEEDED UP ROBUST FEATURES; WIDE AREA SURVEILLANCE; WIDE-AREA MOTION IMAGERIES;

COMPUTATION THEORY; FORESTRY; IMAGE FUSION; IMAGE REGISTRATION; TARGET TRACKING;

IMAGE MATCHING;

ALGORITHMS; COMPUTATION; DATA PROCESSING; EXPERIMENTATION; IMAGE ANALYSIS; SENSORS;

EID: 84874837958     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.918804     Document Type: Conference Paper

References (16)

1. C. Harris and M. Stephens. "A combined corner and edge detector," in Proc. of the 4th Alvey Vision Conference, 1988.
2. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60 91-110, 2004.
3. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded Up Robust Features," in European Conference on Computer Vision (ECCV), 2006.
4. AFRL CLIF 2007 dataset, https://www.sdms.afrl.af.mil/index.php? collection=clif2007
5. M. Muja and D. G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009.
6. C. Silpa-Anan and R. Hartley, "Optimized KD-trees for fast image descriptor matching," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
7. K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points," in IEEE International Conference on Computer Vision (ICCV), 2001.
8. M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Commun. ACM, 24(6):381-395, 1981.
9. O. Mendoza-Schrock, J. A. Patrick, and E. P. Blasch, "Video image registration evaluation for a layered sensing environment," IEEE Aerospace & Electronics Conference (NAECON), 2009.
10. E. K. Kao, M. P. Daggett, and M. B. Hurley. "An information theoretic approach for tracker performance evaluation," International Conference on Computer Vision, 1523-1529, 2009.
11. H. Ling, Y. Wu, E. Blasch, G. Chen, H. Lang, and L. Bai. "Evaluation of Visual Tracking in Extremely Low Frame Rate Wide Area Motion Imagery." In Proc. of the Int'l Conf. on Information Fusion (FUSION), 2011.
12. P. Liang, G, Teodoro, H. Ling, E. Blasch, G. Chen, and L. Bai, "Multiple Kernel Learning for Vehicle Detection in Wide Area Motion Imagery," submitted to Int. Conf. on Info Fusion, 2012.
13. Y. Wu, E. Blasch, G. Chen, L. Bai, and H. Ling, "Multiple Source Data Fusion via Sparse Representation for Robust Visual Tracking," Int. Conf. on Info Fusion, 2011.
14. Y. Wu, J. Wang, J. Cheng, H. Lu, E. Blasch, L. Bai, and H. Ling, "Real-Time Probabilistic Covariance Tracking with Efficient Model Update," IEEE Trans. on Image Processing, April 2012.
15. E. M. Lavely, M. Barmin, V. Kaufman, and E. Blasch, "Model-based, multi-sensor fusion and bundle-adjustment for image registration and target recognition," Proc. of SPIE 6237, 2006.
16. Z. Liu, E. Blasch, Z. Xue, R. Langaniere, and W. Wu, "Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Survey," IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1):94-109, 2012.