Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features


KIPS Transactions on Software and Data Engineering, Vol. 17, No. 5, pp. 333-344, May. 2010
10.3745/KIPSTB.2010.17.5.333, Full Text:

Abstract

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using 640×480 images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.


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Cite this article
[IEEE Style]
K. W. Kwon, H. Y. Lee and D. H. Oh, "Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features," KIPS Journal B (2001 ~ 2012) , vol. 17, no. 5, pp. 333-344, 2010. DOI: 10.3745/KIPSTB.2010.17.5.333.

[ACM Style]
Ki Won Kwon, Hae Yeoun Lee, and Duk Hwan Oh. 2010. Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features. KIPS Journal B (2001 ~ 2012) , 17, 5, (2010), 333-344. DOI: 10.3745/KIPSTB.2010.17.5.333.