Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity


This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.

Share and Cite:

C. Chen, K. Peng, C. Huang and C. Yeh, "Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 114-119. doi: 10.4236/jsip.2013.43B020.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. S. Chen, “A Novel Fourier Descriptor Based Image Alignment Algorithm for Automatic Optical Inspection,” Journal of Visual Communication and Image Representation, 2009, pp. 178-189. doi:10.1016/j.jvcir.2008.11.003
[2] S. D. Wei and S. H. Lai, “Fast Template Matching Based on Normalized Cross Correlation with Adaptive Multilevel Winner Update,” IEEE Transactions on Image Processing, Vol. 17, No. 11, 2008, pp. 2227-2235. doi:10.1109/TIP.2008.2004615
[3] C. S. Chen, C. L. Huang and C. W. Yeh, “An Efficient Sub-Pixel Image Alignment Algorithm Based on Fourier Descriptor,” Advanced Science Letters, Vol. 9, No. 1, 2012, pp. 762-766. doi:10.1166/asl.2012.2537
[4] C. Steger, “Similarity Measures for Occlusion, Clutter, and Illumination Invariant Object Recognition,” Lecture Notes in Computer Science, Vol. 2191, 2001, pp. 148-154.
[5] H. P. Moravec, “Toward Automatic Visual Obstacle Avoidance,” Proc. Fifth of International Joint Conference on Artificial Intelligence, Vol. 1, Cambridge, MA, August 1977, pp.584.
[6] C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proc. of 4th Alvey Vision Conference, Manchester, 31 August–2 September 1988, pp. 147-151.
[7] S. M. Smith and J. M. Brady, “SUSAN-A New Approach to Low Level Image Processing,” International Journal of Computer Vision, Vol. 23, No. 1,1997, pp. 45-78. doi:10.1023/A:1007963824710
[8] T. T. H. Tran and E. Marchand, “Real-Time Key points Matching: Application to Visual Servoing,” IEEE Conference on Robotics and Automation, Roma, 10-14 April 2007, pp. 3787-3792.
[9] C. S. Chen, Y. H. Ku and S. H. Tsai, “Fast Object Recognition Based on Corner Geometric Relationship,” SICE Annual Conference, Taipei, 18-21 August 2010, pp. 1523-1528.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.