A New Fast Similarity Metric Algorithm Based on Contour

Abstract:

The similarity metric is a key on image registration. This paper divides similarity metric algorithms into two classes: similarity metrics based on pixels (or voxels) and similarity metrics based on image features. For those images that acquired contours easily, this paper proposes a new fast similarity metric arithmetic based on scan line. This algorithm is insensitive to illumination change and is robust without considering gray level of pixels (or voxels). In addition, this arithmetic does not consider all pixels (or voxels) in image, but consider pixels (or voxels) in the range of contour. So it is very simple and fast. It is not only suitable for 2D images but also suitable for higher dimension images. In experiment we use Laplacian pyramid to decompose image and use snake model to detect image contour. Lastly we give a novel registration result.

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 2034-2037
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.2034
• Online since: August 2012
• Authors: Jing Jing Wang, Hong Jun Wang, Yong Yin
• Keywords: Fast Similarity Metric Algorithm, Image Registration, Laplacian Pyramid, Scan Line, Snake Model

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