Title :
Panorama recovery from noisy UAV surveillance video
Author :
Wang, Yi ; Schultz, Richard R. ; Fevig, Ronald A.
Author_Institution :
Dept. of Electr. Eng., Univ. of North Dakota, ND
Abstract :
This paper proposes an efficient and robust algorithm to recover a panorama from poorly-obtained UAV video frames contaminated with significant noise. In this algorithm, the eigen-space based neighborhood region will be introduced with our novel feature-based random M least-squares (RMLS) registration technique. Meanwhile, the corresponding similarity regions will be assigned weights according to the relativity between these neighboring regions. Next, Bayesian multi-frame sampling will be implemented utilizing the homography estimated by the frame registration. Finally, the sub-region in each frame which is applicable to the multi-frame sampling will be stitched utilizing multi-resolution blending.
Keywords :
Bayes methods; aerospace computing; eigenvalues and eigenfunctions; feature extraction; image denoising; image reconstruction; image registration; image resolution; image sampling; least squares approximations; random processes; remotely operated vehicles; video surveillance; Bayesian multiframe sampling; eigen-space based neighborhood region; feature-based random M least-squares registration technique; homography; multiresolution blending; noisy UAV surveillance video; panorama recovery; Bayesian methods; Equations; Least squares approximation; Noise reduction; Noise robustness; Payloads; Sampling methods; State estimation; Surveillance; Unmanned aerial vehicles; Mosaic; Multi-scale Neighborhood Region; Random M Least-squares; Unmmaned Aerial Vehicle;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4959826
