Image matching based on orientation-magnitude histograms and global consistency

Authors:
Jianning Liang;Zhenmei Liao;Su Yang;Yuanyuan Wang
Affiliations:
Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China and School of Computer Science and Technology, Fudan University, Shanghai 20 ...;School of Computer Science and Technology, Fudan University, Shanghai 201203, China;School of Computer Science and Technology, Fudan University, Shanghai 201203, China;Department of Electronic Engineering, Fudan University, Shanghai 200433, China
Venue:
Pattern Recognition
Year:
2012

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Abstract

A novel image matching method based on the gradient space is proposed. Image pyramid combined with the Hessian matrix is used to detect scale-invariant interesting points. A new descriptor i.e. an orientation-magnitude histogram is introduced to describe the image content around an interesting point. The proposed local descriptor is proved to be invariant to image rotation. Since the matching result based on the similarities of the descriptors of interesting points always contains outliers, a steepest descent method that optimizes the global consistency of interesting points is presented to remove false matches. The experiments show that the proposed approach is invariant to rotation and scale, robust to the variation of focal lengths, illumination change, occlusion, noises and image blur. Our approach shows better performance than SIFT on multi-view and affine-transformation images. The application of the proposed method to image registration exhibits a good result.