基于不变特征和映射抑制的航拍视频图像配准

Abstract

Abstract: The Harris detector can produce false corners under common photometric transformations and geometric transformations, and the matching points have different accuracy. This paper presents an aerial video image registration scheme by combining the invariant features and mapping restraint. Firstly, the locally most stable points are selected by the multi-resolution Harris detector, and a subarea detection method is presented to determine the well-distributed corners. The descriptors are established at each point in a circular neighborhood. Then Euclidean distance of the corner descriptors are compared to extract the initial feature point pair. Then the Delaunay triangulation of matching point pairs are computed, and the most "useful" matching points that best satisfy the cross-ratio invariants are presented to estimate the geometry transformation, and the image registration is completed. Experimental results demonstrate that the proposed algorithm can effectively offer a robust and accurate image registration for aerial video images.

Key words: aerial video, image registration, well-distributed Harris, local Invariant descriptor, mapping transformation

CLC Number:

V243

YI Meng, GUO Baolong. Aerial Video Image Registration Method Based on Invariant Features and Mapping Restraint[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(10): 1872-1880.

References

[1] Morse B S, Gerhardt D, Engh C, et al. Application and evaluation of spatiotemporal enhancement of live aerial video using temporally local mosaics. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[2] Ding M, Lyngbaek K, Zakhor A. Automatic registration of aerial imagery with untextured 3D LiDAR models. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[3] Liu Z X, An J B, Li L. A two-stage registration algorithm for oil spill aerial image by invariants-based similarity and improved ICP. International Journal of Remote Sensing, 2011, 32(13): 3649-3664.
[4] Niu L P, Mao S Y, Chen W. Multi-sensor image registration method adapted for larger scale. Acta Aeronautica et Astronautica Sinica, 2006, 27(3): 475-480. (in Chinese) 牛力丕,毛士艺, 陈炜. 一种适应较大比例变化的多传感器图像配准方法. 航空学报, 2006, 27(3): 475-480.
[5] Wang L, Neumann U. A robust approach for automatic registration of aerial images with untextured aerial LiDAR data. IEEE Conference on Computer Vision and Pattern Recognition, 2009: 2623-2630.
[6] Liu B Q, Feng D Z, Wu N, et al. An image automatic registration method for InSAR complex images based on point features. Acta Aeronautica et Astronautica Sinica, 2007, 28(1): 161-166. (in Chinese) 刘宝泉, 冯大政, 武楠, 等. 基于点特征的干涉合成孔径雷达复图像自动配准算法. 航空学报, 2007, 28(1): 161-166.
[7] Yang Z L, Guo B L. Image mosaic technique based on pseudo-Zernike moments of interest points. Chinese Journal of Lasers, 2007, 34(11): 1548-1552. (in Chinese) 杨占龙, 郭宝龙. 基于兴趣点伪泽尼克矩的图像拼接.中国激光, 2007, 34(11): 1548-1552.
[8] Szeliski R. Computer vision: algorithms and applications. New York: Springer, 2010.
[9] Brown M, Lowe D G. Automatic panoramic image stitching using invariant features. International Journal of Computer Vision, 2007, 74(1): 59-73.
[10] Song Z L, Zhang J P. Remote sensing image registration based on retrofitted SURF algorithm and trajectories generated from lissajous figures. Geoscience and Remote Sensing Letters, 2010, 7(3): 491-495.
[11] Brown M, Hartley R I, Nister D. Minimal solutions for panoramic stitching. IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.
[12] Harris C, Stephens M J. A combined corner and edge detector. Preceedings of the Fourth Alvey Vision Conference, 1988: 147-151.
[13] Tola E, Lepetit V, Fua P. A fast local descriptor for dense matching. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-8.
[14] Mundy J L, Zisserman A. Geometric invariance in computer vision. Cambridge: MIT Press, 1992: 354-374.
[15] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.

Aerial Video Image Registration Method Based on Invariant Features and Mapping Restraint

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 9

Recommended Articles

Metrics

Comments

[1]	LIU Zhongjie, CAO Yunfeng, ZHUANG Likui, DING Meng. Applied Research on Airborne SAR and Optical Image Registration Based on Control Line Method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(9): 2194-2201.
[2]	SHEN Hao, LI Shuxiao, SHEN Yiping, ZHU Chengfei, CHANG Hongxing. Fast Interframe Registration Method in Aerial Videos [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(6): 1405-1413.
[3]	LIAO Chao, WANG Guijin, SHEN Yongling, HE Bei, LIN Xinggang. Aerial Video Stitching via Multi-direction Strips [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(11): 2065-2073.
[4]	LONG Yunli, XU Hui, AN Wei, LIN Liangkui. Spatial-temporal Fused Filtering for Infrared Clutter Suppression Based on Restricted Sequential M-estimation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(8): 1531-1541.
[5]	Wang Yunli;Gao Chao;Zhang Maojun. A Gradient-domain Method to Blend Multiple Images for Aerial Video Mosaic [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2009, 30(9): 1705-1710.
[6]	Wang Yunli;Zhang Xin;Gao Chao;Wang Hui;Zhang Maojun. Feature Matching Based Global Motion Estimation in Aerial Video Mosaicing [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2008, 29(5): 1218-1225.
[7]	NIU Li-pi;MAO Shi-yi;CHEN Wei. Multi-Sensor Image Registration Method Adapted for Larger Scale [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(3): 475-480.
[8]	XU Dong;AN Jin-wen. Global Motion Estimation by Fitting Optical Flow in Aerial Video Imagery [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(1): 94-97.
[9]	WANG Yue;MAO Shi-yi;CHEN Wei. A Multiresolution Algorithm for Image Registration with Partial Hausdorff Distance [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2004, 25(6): 593-597.