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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 667-671 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.211

Image Registration Based On a Novel Enhanced Scale Invariant Geometrical Feature

Ehsan Hossein KalatehJari, Mohammad Mehdi Hosseini, and Abdorreza Alavi Gharahbagh
Abstract—Image registration can find similarities between one image and another one from the same scene taken by different angles. Registration is widely used in robot localization, remote sensing, medical imaging and etc. In this paper, a new method based on the image geometrical features has introduced by using two important characteristics of image scale and image rotation. Feature points of two images by SIFT algorithm has extracted. Then, an initial matching is estimated based on descriptor matrix of SIFT features with nearest neighbor (NN) criterion. The novel geometrical method is used for discarding incorrect matched points. Finally, the correct image is recognized by the number of matched points.

Index Terms—Nearest neighbor, sift, geometrical transform, image registration.

The authors are with the department of electrical and computer engineering, Islamic azad university, shahrood branch, shahrood, Iran. (e-mail: ehsankl@yahoo.com; Hoseini_mm@yahoo.com; dramalavi_gharah@yahoo.com).

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Cite:Ehsan Hossein KalatehJari, Mohammad Mehdi Hosseini, and Abdorreza Alavi Gharahbagh, "Image Registration Based On a Novel Enhanced Scale Invariant Geometrical Feature," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 667-671, 2012.

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