Improvement of Image Mosaic Algorithm Based on SURF

Abstract:

The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations of the traditional speeded up robust features mosaic method, improving the matching accuracy and speed, and the mosaic effect. It has certain theoretical and practical value.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1625-1630
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1625
• Online since: September 2013
• Authors: Xu Lin Long*, Qiang Chen, Jun Wei Bao
• Keywords: Feature Clustering, Image Matching, Image Mosaic, SURF (Speeded up Robust Features)

References

[1] LUO S, HE J. Mosaic Algorithm of Image Based on SURF and KLT Track[J]. Computer Engineering, 2010, 36(1): 215-217.

[2] Liu Q, HE M. Image stitching based on surf feature matching[J]. Measurement & Control Technology, 2010, 10: 008.

[3] YANG Yun-tao, FENG Ying, CAO Yu, CHEN Yun-jin. Fast Method for Image Sequences Mosaic Based on SURF[J]. Computer Technology and Development, 2011, 21(3): 6-9.

[4] Rui R Q P G L. Study on Image Registration and Mosaic Technologhy Based on Surf Feature[J]. Computer & Digital Engineering, 2011, 2: 037.

[5] Zitova B, Flusser J. Image registration methods: a survey[J]. Image and vision computing, 2003, 21(11): 977-1000.

DOI: 10.1016/s0262-8856(03)00137-9

[6] LI Q, WANG G, LIU J, CHEN S. Multispectral Image Registration Method Based on Local Feature Clustering Match[J]. Computer Engineering, 2009, 15: 005.

[7] QI Shi-Gui , QI Su-Juan. Image Registration by NN Based on Features [J]. Journal of XuChang University, 2008, 27(2): 67.

[8] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

[9] Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features[M]/Computer Vision–ECCV 2006. Springer Berlin Heidelberg, 2006: 404-417.

DOI: 10.1007/11744023_32

[10] GU Da-long, ZENG Luan, ZHAI You. Improvement of image registration algorithm based on SURF[J]. Modern Electronics Technique, 2012, 35(14): 79-82.

[11] ZHAO L, GEN G, LI K, HE A. Images matching algorithm based on SURF and fast approximate nearest neighbor search[J]. Aplication Research of Computers, 2013, 30(3): 921-923.

[12] LI H, NIU J, Guo H E. Automatic seamless image mosaic method based on feature points [J]. Computer Engineering and Design, 2007, 9: 031.

[13] WU J, GUO B, FENG Z. An image mosaic method based on interest points matching [J]. Journal of Computer Applications, 2006, 3: 031.

[14] ZHANG R, ZHANG J, YANG C. Image registration approach based on SURF [J]. Infrared and Laser Engineering, 2009, 1: 041.

[15] Szeliski R. Video mosaics for virtual environments[J]. Computer Graphics and Applications, IEEE, 1996, 16(2): 22-30.

DOI: 10.1109/38.486677