An Image Registration Algorithm Based on Improved SIFT Feature

Abstract:

Aiming at there are long matching time and many wrong matching in the traditional SIFT algorithm, An image registration algorithm based on improved SIFT feature is put forward. First of all, through setting the number of extreme points in the feature point detection, feature points is found according to the DOG space structure from coarse to fine, and the improved SIFT feature descriptor generation algorithm is used. The preliminary matched point pairs are obtained by the nearest neighbor matching criterion, and the bilateral matching method is used for screening the preliminary matched point. Then, the second matching will be done by the similar measurement method based on mahalanobis distance, and RANSAC algorithm is used to calculate the affine transform model. Finally, the transformed image is resampled and interpolated through the bilinear interpolation method. Experimental results show that the algorithm can realize image registration effectively. Image registration technique is an important research content in computer vision and image processing in the, which are widely used in vehicle matching navigation and positioning, cruise missile terminal guidance, target tracking and recognition, image mosaic^[1-6]. SIFT algorithm^[3-5] can achieve image registration when there are translation, rotation, affine transformation between two images, even for images took by arbitrary angles. And SIFT feature is the milestone of local feature study. But there are long matching time and many wrong matching in the traditional SIFT algorithm, it is difficult to meet the requirement of fast image registration. This paper puts forward an image registration algorithm based on improved SIFT feature, which is robust for image rotation, affine and scale change, and is better than traditional SIFT algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3411-3415
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3411
• Online since: August 2013
• Authors: Yin Wen Dong, Luan Wan, Zhao Ming Shi, Ming Lei Zhu
• Keywords: Feature Descriptor, Image Registration, Lateral Matching, Mahalanobis Distance, SIFT

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