Fast stitch algorithm on aerial images

Because of high resolution of the aerial images, the existing stitching algorithms' speed are too slow. In this paper, we propose a fast stitching algorithm. First, we build Gaussian pyramid and select the key pyramid image by image characteristic. Then, we extract feature points and use region removal algorithm to get the key feature matches of an image. Region removal algorithm selects key feature match in a circle region and removes the redundant quantity. It improves the efficiency of image stitching. Finally, we analyze the relationship between the total errors with iteration times of bundle adjustment, which shows that the errors decrease fast at the first 50 iterations. Experiments on two datasets show that, our algorithm improves both the speed and precision.

[1]  A. Ardeshir Goshtasby,et al.  Precision Registration and Mosaicking of Multicamera Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Michael S. Brown,et al.  As-Projective-As-Possible Image Stitching with Moving DLT , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Li Liang-qun,et al.  A New Fast Automatic Mosaic Method on Unmanned Aerial Vehicle Images , 2012 .

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Yu Hen Hu,et al.  Discovering panoramas in web videos , 2008, ACM Multimedia.

[6]  Dieter Schmalstieg,et al.  Real-time self-localization from panoramic images on mobile devices , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[7]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Shanaka Ransiri,et al.  A faster image registration and stitching algorithm , 2011, 2011 6th International Conference on Industrial and Information Systems.

[9]  Lu Wei Automatic Image Matching Based on Wavelet Pyramid , 2002 .

[10]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[11]  Paul W. Fieguth,et al.  Fast phase-based registration of multimodal image data , 2009, Signal Process..

[12]  Luo Juan,et al.  SURF applied in panorama image stitching , 2010, 2010 2nd International Conference on Image Processing Theory, Tools and Applications.

[13]  Wei-Yen Hsu,et al.  Automatic seamless mosaicing of microscopic images: enhancing appearance with colour degradation compensation and wavelet‐based blending , 2008, Journal of microscopy.

[14]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Michael S. Brown,et al.  Constructing image panoramas using dual-homography warping , 2011, CVPR 2011.

[16]  Gordon Wetzstein,et al.  Computational Plenoptic Imaging , 2011, Comput. Graph. Forum.

[17]  Guo Bao-long,et al.  A Fast Automatic Image Stitching Algorithm , 2005 .

[18]  Haifeng Zhao,et al.  New multi-resolution image stitching with local and global alignment , 2010 .

[19]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.