A New Method Based on Image Registration Algorithm

Abstract:

We introduce a new algorithm for Image Registration and Stitching. The algorithm is designed to be extremely efficient and fast in its execution and is intended for use in stitching images extracted from a video stream of a camera. This algorithm is not universally applicable to all the image registration and stitching problems. It is customized to be used to generate single images of surfaces such as a conveyor belt or undercarriage of vehicles, which cannot be captured by a single photo. The algorithm works by extracting edges of the two images to be registered. Then it selects a reference section from the first image and search in the second image where it finds the best match for that section. The best match is the east difference score. We present full details of how the extraction of the heuristic is done from the inputs and how it drastically reduces the execution time of the algorithm. The paper also contains a full section on comparing our algorithm with a set of existing algorithms. Our algorithm outperforms the existing ones for all the common image sizes.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 3305-3308
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.3305
• Online since: May 2014
• Authors: Wei Wei Hu*
• Keywords: Image Registration, Image Stitching, Quick Check

References

[1] R. Szeliski, Image Alignment and Stitching, A Tutorial, Technical Report MSR-TR-2004-92, Microsoft Research, Microsoft Corporation, (2004).

[2] R. Szeliski and S. Kang. Direct methods for visual scene reconstruction., In IEEE Workshop on Representations of Visual Scenes, pages 26–33, Cambridge, MA, (1995).

DOI: 10.1109/wvrs.1995.476849

[3] H. Sawhney and R. Kumar. True multi-image alignment and its application to mosaicing and lens distortion correction., IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(3): 235–243, (1999).

DOI: 10.1109/34.754589

[4] J. Shi and C. Tomasi. Good features to track., In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR94), Seattle, June (1994).

[5] Guo Wenjun, Ramon Gilsanz. Nonlinear static analysis procedure) Progressive collapse evaluation [R ]. Design Engineers of GilsanzMurray Steficek, LLP., 2003: 1-11.

[6] Corley W G. Applicability of seismic design in mitigating progressive collapse [R/OL]. [2006-09-20]. http: /www. nibs. org M/ MC.